Use recipe ingredients to categorize the cuisine
Picture yourself strolling through your local, open-air market... What do you see? What do you smell? What will you make for dinner tonight?
If you're in Northern California, you'll be walking past the inevitable bushels of leafy greens, spiked with dark purple kale and the bright pinks and yellows of chard. Across the world in South Korea, mounds of bright red kimchi greet you, while the smell of the sea draws your attention to squids squirming nearby. India’s market is perhaps the most colorful, awash in the rich hues and aromas of dozens of spices: turmeric, star anise, poppy seeds, and garam masala as far as the eye can see.
Some of our strongest geographic and cultural associations are tied to a region's local foods. This playground competitions asks you to predict the category of a dish's cuisine given a list of its ingredients.
재료의 목록을 보고 요리의 범주(국가)를 예측하는 문제.
json 형태 파일
{
"id": 24717,
"cuisine": "indian",
"ingredients": [
"tumeric",
"vegetable stock",
"tomatoes",
"garam masala",
"naan",
"red lentils",
"red chili peppers",
"onions",
"spinach",
"sweet potatoes"
]
},
train.json - the training set containing recipes id, type of cuisine, and list of ingredients
test.json - the test set containing recipes id, and list of ingredients
sample_submission.csv - a sample submission file in the correct format
평가방법 (Submissions are evaluated on the categorization accuracy (the percent of dishes that you correctly classify).
set.seed(2310)
install.packages(c('rzmq','repr','IRkernel','IRdisplay'), repos = 'http://irkernel.github.io/')
바이너리 버전을 이용할 수 있습니다 (그리고 설치되어질 것입니다) 그러나 소스 버전은 추후에 제공될 것입니다: binary source repr 0.3 0.4
Warning message: In download.file(url, destfile, method, mode = "wb", ...): cannot open: HTTP status was '400 Bad Request'Warning message: In download.packages(pkgs, destdir = tmpd, available = available, : download of package 'rzmq' failed
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IRkernel::installspec()
install.packages("jsonlite", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("dplyr", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("ggplot2", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("tm", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("caret", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("rpart.plot", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("SnowballC", dependencies=T, repos='http://cran.rstudio.com/')
also installing the dependencies 'memoise', 'mime', 'bitops', 'Rcpp', 'crayon', 'praise', 'evaluate', 'formatR', 'highr', 'markdown', 'yaml', 'htmltools', 'caTools', 'R.methodsS3', 'R.oo', 'R.utils', 'R.cache', 'curl', 'plyr', 'testthat', 'knitr', 'rmarkdown', 'R.rsp'
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also installing the dependencies 'colorspace', 'RColorBrewer', 'dichromat', 'munsell', 'labeling', 'chron', 'gtable', 'reshape2', 'scales', 'proto', 'assertthat', 'R6', 'magrittr', 'lazyeval', 'DBI', 'RSQLite', 'RMySQL', 'RPostgreSQL', 'data.table', 'microbenchmark', 'ggplot2', 'Lahman', 'nycflights13'
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also installing the dependencies 'zoo', 'SparseM', 'MatrixModels', 'Formula', 'latticeExtra', 'acepack', 'gridExtra', 'sp', 'mvtnorm', 'TH.data', 'sandwich', 'quantreg', 'Hmisc', 'mapproj', 'maps', 'hexbin', 'maptools', 'multcomp'
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Warning message: : dependencies 'Rcampdf', 'Rgraphviz', 'Rpoppler', 'tm.lexicon.GeneralInquirer' are not availablealso installing the dependencies 'NLP', 'slam', 'filehash', 'SnowballC', 'XML'
package 'NLP' successfully unpacked and MD5 sums checked package 'slam' successfully unpacked and MD5 sums checked package 'filehash' successfully unpacked and MD5 sums checked package 'SnowballC' successfully unpacked and MD5 sums checked package 'XML' successfully unpacked and MD5 sums checked package 'tm' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages
Warning message: : dependency 'rPython' is not availablealso installing the dependencies 'numDeriv', 'minqa', 'nloptr', 'profileModel', 'plotrix', 'lava', 'pbkrtest', 'iterators', 'lme4', 'brglm', 'gtools', 'plotmo', 'TeachingDemos', 'prodlim', 'combinat', 'modeltools', 'strucchange', 'coin', 'car', 'foreach', 'BradleyTerry2', 'e1071', 'earth', 'fastICA', 'gam', 'ipred', 'kernlab', 'klaR', 'ellipse', 'mda', 'mlbench', 'party', 'pls', 'pROC', 'proxy', 'randomForest', 'RANN', 'spls', 'subselect', 'pamr', 'superpc', 'Cubist'
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library(jsonlite)
library(dplyr)
library(ggplot2)
library(tm) # For NLP; creating bag-of-words
library(caret)
library(rpart)
library(rpart.plot)
library(SnowballC)
Error in library(jsonlite): there is no package called 'jsonlite'
Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Loading required package: NLP Attaching package: 'NLP' The following object is masked from 'package:ggplot2': annotate Loading required package: lattice
load("json.Rdata")
ls()
head(train)
id | cuisine | ingredients | |
---|---|---|---|
1 | 10259 | greek | romaine lettuce, black olives, grape tomatoes, garlic, pepper, purple onion, seasoning, garbanzo beans, feta cheese crumbles |
2 | 25693 | southern_us | plain flour, ground pepper, salt, tomatoes, ground black pepper, thyme, eggs, green tomatoes, yellow corn meal, milk, vegetable oil |
3 | 20130 | filipino | eggs, pepper, salt, mayonaise, cooking oil, green chilies, grilled chicken breasts, garlic powder, yellow onion, soy sauce, butter, chicken livers |
4 | 22213 | indian | water, vegetable oil, wheat, salt |
5 | 13162 | indian | black pepper, shallots, cornflour, cayenne pepper, onions, garlic paste, milk, butter, salt, lemon juice, water, chili powder, passata, oil, ground cumin, boneless chicken skinless thigh, garam masala, double cream, natural yogurt, bay leaf |
6 | 6602 | jamaican | plain flour, sugar, butter, eggs, fresh ginger root, salt, ground cinnamon, milk, vanilla extract, ground ginger, powdered sugar, baking powder |
ID 요리 재료
1 10259 그리스어로 메인 양상추, 블랙 올리브, 포도, 토마토, 마늘, 고추, 보라색 양파, 조미료, garbanzo 콩, 죽은 태아의 치즈는 잘게
2 25693 southern_us 일반 밀가루, 후추, 소금, 토마토, 지상 후추, 타임, 계란, 녹색 토마토, 노란 옥수수 가루, 우유, 식물성 기름
3 20130 필리핀 계란, 후추, 소금, mayonaise, 기름, 녹색 고추, 구운 닭 가슴살, 마늘 분말, 노란 양파, 간장, 버터, 닭의 간 요리
4 22213 인도 물, 식물성 기름, 밀가루, 소금
5 13162 인도 후추, 골파, 옥수수 가루, 카이엔 고추, 양파, 마늘 페이스트, 우유, 버터, 소금, 레몬 주스, 물, 고추 가루, passata, 오일, 지상 커 민, 뼈없는 닭 껍질을 벗기는 허벅지, 가람 마살라, 더블 크림, 천연 요구르트, 베이 리프
6 6602 자메이카 일반 밀가루, 설탕, 버터, 계란, 신선한 생강 뿌리, 소금, 분말 계피, 우유, 바닐라 추출물, 지상 생강, 가루 설탕, 베이킹 파우더
head(test)
id | ingredients | |
---|---|---|
1 | 18009 | baking powder, eggs, all-purpose flour, raisins, milk, white sugar |
2 | 28583 | sugar, egg yolks, corn starch, cream of tartar, bananas, vanilla wafers, milk, vanilla extract, toasted pecans, egg whites, light rum |
3 | 41580 | sausage links, fennel bulb, fronds, olive oil, cuban peppers, onions |
4 | 29752 | meat cuts, file powder, smoked sausage, okra, shrimp, andouille sausage, water, paprika, hot sauce, garlic cloves, browning, lump crab meat, vegetable oil, all-purpose flour, freshly ground pepper, flat leaf parsley, boneless chicken skinless thigh, dried thyme, white rice, yellow onion, ham |
5 | 35687 | ground black pepper, salt, sausage casings, leeks, parmigiano reggiano cheese, cornmeal, water, extra-virgin olive oil |
6 | 38527 | baking powder, all-purpose flour, peach slices, corn starch, heavy cream, lemon juice, unsalted butter, salt, white sugar |
ID 성분
1 18009 베이킹 파우더, 계란, 다목적 밀가루, 건포도, 우유, 백설탕
2 28583 설탕, 달걀 노른자, 옥수수 전분, 치석, 바나나, 바닐라 웨이퍼, 우유, 바닐라 추출물, 구운 피칸, 달걀 흰자의 크림, 라이트 럼
3 41580 소시지 링크, 회향 전구, 잎, 올리브 오일, 쿠바 고추, 양파
4 29752 고기 인하, 파일 가루, 훈제 소시지, 오크라, 새우, andouille 소시지, 물, 파프리카, 핫 소스, 다진 마늘, 갈색, 덩어리 게 고기, 식물성 기름, 다목적 밀가루, 갓 지상 후추, 평면 잎 파슬리, 뼈없는 닭 껍질을 벗기는 허벅지, 말린 백리향, 흰 쌀, 노란 양파, 햄
5 35687 지상 후추, 소금, 소시지 케이싱, 부추, 파르 미자의 reggiano 치즈, 옥수수 가루, 물, 엑스트라 버진 올리브 오일
6 38527 베이킹 파우더, 다목적 밀가루, 복숭아 조각, 옥수수 전분, 무거운 크림, 레몬 주스, 무염 버터, 소금, 흰 설탕
sample_sub <- read.csv('sample_submission.csv')
head(sample_sub)
id | cuisine | |
---|---|---|
1 | 35203 | italian |
2 | 17600 | italian |
3 | 35200 | italian |
4 | 17602 | italian |
5 | 17605 | italian |
6 | 17604 | italian |
ingredients <- Corpus(VectorSource(c(train$ingredients,test$ingredients)))
ingredients
<<VCorpus>> Metadata: corpus specific: 0, document level (indexed): 0 Content: documents: 49718
?Corpus #tm package Corpus~
Warning message: In read.dcf(file.path(p, "DESCRIPTION"), c("Package", "Version")): 압축된 파일 'C:/Anaconda/R/library/jsonlite/DESCRIPTION'를 열 수 없습니다. 그 이유는 아마도 'No such file or directory'입니다Warning message: In find.package(if (is.null(package)) loadedNamespaces() else package, : there is no package called 'jsonlite'
Corpus {tm} | R Documentation |
Representing and computing on corpora.
Corpora are collections of documents containing (natural language)
text. In packages which employ the infrastructure provided by package
tm, such corpora are represented via the virtual S3 class
Corpus
: such packages then provide S3 corpus classes extending the
virtual base class (such as VCorpus
provided by package tm
itself).
All extension classes must provide accessors to extract subsets
([
), individual documents ([[
), and metadata
(meta
). The function length
must return the number
of documents, and as.list
must construct a list holding the
documents.
A corpus can have two types of metadata (accessible via meta
).
Corpus metadata contains corpus specific metadata in form of tag-value
pairs. Document level metadata contains document specific metadata but
is stored in the corpus as a data frame. Document level metadata is typically
used for semantic reasons (e.g., classifications of documents form an own
entity due to some high-level information like the range of possible values)
or for performance reasons (single access instead of extracting metadata of
each document).
VCorpus
, and PCorpus
for the corpora classes
provided by package tm.
DCorpus
for a distributed corpus class provided by
package tm.plugin.dc.
ingredients <- tm_map(ingredients, stemDocument)
ingredients
<<VCorpus>> Metadata: corpus specific: 0, document level (indexed): 0 Content: documents: 49718
ingredientsDTM <- DocumentTermMatrix(ingredients)
?DocumentTermMatrix
Warning message: In read.dcf(file.path(p, "DESCRIPTION"), c("Package", "Version")): 압축된 파일 'C:/Anaconda/R/library/jsonlite/DESCRIPTION'를 열 수 없습니다. 그 이유는 아마도 'No such file or directory'입니다Warning message: In find.package(if (is.null(package)) loadedNamespaces() else package, : there is no package called 'jsonlite'
TermDocumentMatrix {tm} | R Documentation |
Constructs or coerces to a term-document matrix or a document-term matrix.
TermDocumentMatrix(x, control = list()) DocumentTermMatrix(x, control = list()) as.TermDocumentMatrix(x, ...) as.DocumentTermMatrix(x, ...)
x |
a corpus for the constructors and either a term-document matrix or a document-term matrix or a simple triplet matrix (package slam) or a term frequency vector for the coercing functions. |
control |
a named list of control options. There are local
options which are evaluated for each document and global options
which are evaluated once for the constructed matrix. Available local
options are documented in
|
... |
the additional argument |
An object of class TermDocumentMatrix
or class
DocumentTermMatrix
(both inheriting from a
simple triplet matrix in package slam)
containing a sparse term-document matrix or document-term matrix. The
attribute Weighting
contains the weighting applied to the
matrix.
termFreq
for available local control options.
data("crude") tdm <- TermDocumentMatrix(crude, control = list(removePunctuation = TRUE, stopwords = TRUE)) dtm <- DocumentTermMatrix(crude, control = list(weighting = function(x) weightTfIdf(x, normalize = FALSE), stopwords = TRUE)) inspect(tdm[202:205, 1:5]) inspect(tdm[c("price", "texas"), c("127", "144", "191", "194")]) inspect(dtm[1:5, 273:276])
ingredientsDTM
<<DocumentTermMatrix (documents: 49718, terms: 2886)>> Non-/sparse entries: 927832/142558316 Sparsity : 99% Maximal term length: 25 Weighting : term frequency (tf)
sparse <- removeSparseTerms(ingredientsDTM, 0.99)
sparse
<<DocumentTermMatrix (documents: 49718, terms: 250)>> Non-/sparse entries: 808758/11620742 Sparsity : 93% Maximal term length: 13 Weighting : term frequency (tf)
?removeSparseTerms
Warning message: In read.dcf(file.path(p, "DESCRIPTION"), c("Package", "Version")): 압축된 파일 'C:/Anaconda/R/library/jsonlite/DESCRIPTION'를 열 수 없습니다. 그 이유는 아마도 'No such file or directory'입니다Warning message: In find.package(if (is.null(package)) loadedNamespaces() else package, : there is no package called 'jsonlite'
removeSparseTerms {tm} | R Documentation |
Remove sparse terms from a document-term or term-document matrix.
removeSparseTerms(x, sparse)
x |
A |
sparse |
A numeric for the maximal allowed sparsity in the range from bigger zero to smaller one. |
A term-document matrix where those terms from x
are
removed which have at least a sparse
percentage of empty (i.e.,
terms occurring 0 times in a document) elements. I.e., the resulting
matrix contains only terms with a sparse factor of less than
sparse
.
data("crude") tdm <- TermDocumentMatrix(crude) removeSparseTerms(tdm, 0.2)
ingredientsDTM <- as.data.frame(as.matrix(sparse))
head(ingredientsDTM)
all-purpos | allspic | almond | and | appl | avocado | babi | bacon | bake | balsam | basil | bay | bean | beansprout | beef | bell | black | boil | boneless | bread | breast | broccoli | broth | brown | butter | buttermilk | cabbag | canola | caper | cardamom | carrot | cayenn | celeri | cheddar | chees | cherri | chicken | chickpea | chile | chili | chines | chip | chipotl | chive | chocol | chop | cider | cilantro | cinnamon | clove | coars | coconut | cold | condens | confection | cook | coriand | corn | cornmeal | crack | cream | crumb | crumbl | crush | cucumb | cumin | curri | dark | dice | dijon | dill | dough | dress | dri | egg | eggplant | enchilada | extra-virgin | extract | fat | fennel | feta | fillet | fine | firm | fish | flake | flat | flour | free | fresh | frozen | garam | garlic | ginger | golden | granul | grate | greek | green | ground | halv | ham | heavi | hoisin | honey | hot | ice | italian | jack | jalapeno | juic | kalamata | kernel | ketchup | kosher | lamb | larg | leaf | lean | leav | leek | lemon | less | lettuc | light | lime | low | low-fat | masala | mayonais | meat | medium | mexican | milk | minc | mint | mirin | mix | monterey | mozzarella | mushroom | mustard | noodl | nutmeg | oil | oliv | onion | orang | oregano | oyster | paprika | parmesan | parsley | past | pasta | pea | peanut | pecan | peel | pepper | peppercorn | plain | plum | pork | potato | powder | purpl | raisin | red | reduc | rib | rice | ricotta | roast | root | rosemari | saffron | sage | salsa | salt | sauc | sausag | scallion | sea | season | seed | serrano | sesam | shallot | sharp | shell | sherri | shiitak | shoulder | shred | shrimp | skinless | slice | smoke | soda | sodium | soup | sour | soy | spaghetti | spinach | spray | sprig | spring | squash | starch | steak | stick | stock | sugar | sweet | sweeten | syrup | taco | thai | thigh | thyme | toast | tofu | tomato | tortilla | tumer | turkey | turmer | unsalt | unsweeten | vanilla | veget | vinegar | warm | water | wedg | wheat | whip | white | whole | wine | worcestershir | yeast | yellow | yogurt | yolk | zest | zucchini | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
ingredientsDTM_train <- ingredientsDTM[1:39774,]
ingredientsDTM_test <- ingredientsDTM[39775:49718,]
dim(ingredientsDTM_train)
dim(ingredientsDTM_test)
ingredientsDTM_train$cuisine <- as.factor(train$cuisine)
ingredientsDTM_test$cuisine <- NA
head(ingredientsDTM_train)
all-purpos | allspic | almond | and | appl | avocado | babi | bacon | bake | balsam | basil | bay | bean | beansprout | beef | bell | black | boil | boneless | bread | breast | broccoli | broth | brown | butter | buttermilk | cabbag | canola | caper | cardamom | carrot | cayenn | celeri | cheddar | chees | cherri | chicken | chickpea | chile | chili | chines | chip | chipotl | chive | chocol | chop | cider | cilantro | cinnamon | clove | coars | coconut | cold | condens | confection | cook | coriand | corn | cornmeal | crack | cream | crumb | crumbl | crush | cucumb | cumin | curri | dark | dice | dijon | dill | dough | dress | dri | egg | eggplant | enchilada | extra-virgin | extract | fat | fennel | feta | fillet | fine | firm | fish | flake | flat | flour | free | fresh | frozen | garam | garlic | ginger | golden | granul | grate | greek | green | ground | halv | ham | heavi | hoisin | honey | hot | ice | italian | jack | jalapeno | juic | kalamata | kernel | ketchup | kosher | lamb | larg | leaf | lean | leav | leek | lemon | less | lettuc | light | lime | low | low-fat | masala | mayonais | meat | medium | mexican | milk | minc | mint | mirin | mix | monterey | mozzarella | mushroom | mustard | noodl | nutmeg | oil | oliv | onion | orang | oregano | oyster | paprika | parmesan | parsley | past | pasta | pea | peanut | pecan | peel | pepper | peppercorn | plain | plum | pork | potato | powder | purpl | raisin | red | reduc | rib | rice | ricotta | roast | root | rosemari | saffron | sage | salsa | salt | sauc | sausag | scallion | sea | season | seed | serrano | sesam | shallot | sharp | shell | sherri | shiitak | shoulder | shred | shrimp | skinless | slice | smoke | soda | sodium | soup | sour | soy | spaghetti | spinach | spray | sprig | spring | squash | starch | steak | stick | stock | sugar | sweet | sweeten | syrup | taco | thai | thigh | thyme | toast | tofu | tomato | tortilla | tumer | turkey | turmer | unsalt | unsweeten | vanilla | veget | vinegar | warm | water | wedg | wheat | whip | white | whole | wine | worcestershir | yeast | yellow | yogurt | yolk | zest | zucchini | cuisine | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | greek |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | southern_us |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | filipino |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | indian |
5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | indian |
6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | jamaican |
inTrain <- createDataPartition(y = ingredientsDTM_train$cuisine, p = 0.6, list = FALSE)
train_data <- ingredientsDTM_train[inTrain,]
vaild_data <- ingredientsDTM_train[-inTrain,]
test_data <- ingredientsDTM_test
dim(train_data)
dim(vaild_data)
ingredients_sum <- apply(rbind(ingredientsDTM_train[,-251], ingredientsDTM_test[,-251]), 2, sum)
head(ingredients_sum)
ingredients_sum <- ingredients_sum[order(ingredients_sum, decreasing=T)]
names(ingredients_sum)
plot(ingredients_sum[1:15], xaxt='n')
axis(side=1, at=1:15, labels=names(ingredients_sum[1:15]), col.axis="blue", cex.axis=0.5)
barplot(ingredients_sum[1:15], las=2)
barplot(ingredients_sum[30:50], las=2)
aggregate(ingredientsDTM_train[,c(1:250)], list(ingredientsDTM_train[,251]), sum)
Group.1 | all-purpos | allspic | almond | and | appl | avocado | babi | bacon | bake | balsam | basil | bay | bean | beansprout | beef | bell | black | boil | boneless | bread | breast | broccoli | broth | brown | butter | buttermilk | cabbag | canola | caper | cardamom | carrot | cayenn | celeri | cheddar | chees | cherri | chicken | chickpea | chile | chili | chines | chip | chipotl | chive | chocol | chop | cider | cilantro | cinnamon | clove | coars | coconut | cold | condens | confection | cook | coriand | corn | cornmeal | crack | cream | crumb | crumbl | crush | cucumb | cumin | curri | dark | dice | dijon | dill | dough | dress | dri | egg | eggplant | enchilada | extra-virgin | extract | fat | fennel | feta | fillet | fine | firm | fish | flake | flat | flour | free | fresh | frozen | garam | garlic | ginger | golden | granul | grate | greek | green | ground | halv | ham | heavi | hoisin | honey | hot | ice | italian | jack | jalapeno | juic | kalamata | kernel | ketchup | kosher | lamb | larg | leaf | lean | leav | leek | lemon | less | lettuc | light | lime | low | low-fat | masala | mayonais | meat | medium | mexican | milk | minc | mint | mirin | mix | monterey | mozzarella | mushroom | mustard | noodl | nutmeg | oil | oliv | onion | orang | oregano | oyster | paprika | parmesan | parsley | past | pasta | pea | peanut | pecan | peel | pepper | peppercorn | plain | plum | pork | potato | powder | purpl | raisin | red | reduc | rib | rice | ricotta | roast | root | rosemari | saffron | sage | salsa | salt | sauc | sausag | scallion | sea | season | seed | serrano | sesam | shallot | sharp | shell | sherri | shiitak | shoulder | shred | shrimp | skinless | slice | smoke | soda | sodium | soup | sour | soy | spaghetti | spinach | spray | sprig | spring | squash | starch | steak | stick | stock | sugar | sweet | sweeten | syrup | taco | thai | thigh | thyme | toast | tofu | tomato | tortilla | tumer | turkey | turmer | unsalt | unsweeten | vanilla | veget | vinegar | warm | water | wedg | wheat | whip | white | whole | wine | worcestershir | yeast | yellow | yogurt | yolk | zest | zucchini | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | brazilian | 18 | 1 | 24 | 2 | 10 | 10 | 3 | 30 | 19 | 0 | 2 | 54 | 71 | 0 | 31 | 80 | 133 | 4 | 16 | 13 | 17 | 0 | 33 | 21 | 83 | 2 | 0 | 13 | 1 | 0 | 26 | 23 | 7 | 5 | 56 | 5 | 76 | 0 | 23 | 41 | 0 | 4 | 5 | 2 | 35 | 76 | 2 | 88 | 14 | 92 | 7 | 142 | 12 | 68 | 1 | 28 | 22 | 34 | 4 | 3 | 35 | 9 | 0 | 32 | 2 | 29 | 2 | 5 | 21 | 4 | 0 | 1 | 0 | 62 | 105 | 0 | 0 | 13 | 18 | 5 | 3 | 0 | 25 | 8 | 0 | 27 | 33 | 10 | 73 | 5 | 133 | 32 | 1 | 194 | 31 | 1 | 21 | 21 | 0 | 85 | 126 | 3 | 17 | 7 | 0 | 12 | 22 | 57 | 5 | 3 | 19 | 104 | 0 | 4 | 1 | 22 | 2 | 33 | 33 | 1 | 63 | 2 | 37 | 3 | 1 | 16 | 164 | 10 | 5 | 1 | 10 | 3 | 10 | 0 | 229 | 15 | 7 | 0 | 6 | 2 | 5 | 2 | 5 | 0 | 5 | 247 | 145 | 226 | 50 | 14 | 1 | 19 | 24 | 55 | 21 | 1 | 16 | 10 | 3 | 6 | 310 | 1 | 3 | 9 | 35 | 26 | 63 | 8 | 6 | 86 | 2 | 18 | 63 | 0 | 11 | 4 | 2 | 3 | 1 | 7 | 246 | 35 | 30 | 8 | 19 | 7 | 26 | 7 | 2 | 5 | 1 | 2 | 3 | 0 | 9 | 15 | 58 | 10 | 22 | 16 | 8 | 16 | 2 | 6 | 3 | 0 | 6 | 3 | 1 | 4 | 2 | 22 | 12 | 4 | 29 | 150 | 18 | 61 | 16 | 0 | 1 | 6 | 13 | 5 | 1 | 133 | 1 | 3 | 5 | 4 | 26 | 29 | 28 | 46 | 26 | 2 | 112 | 15 | 3 | 4 | 91 | 15 | 30 | 2 | 6 | 34 | 3 | 22 | 8 | 3 |
2 | british | 238 | 27 | 44 | 3 | 30 | 0 | 2 | 25 | 217 | 17 | 5 | 42 | 15 | 0 | 158 | 4 | 134 | 18 | 3 | 81 | 4 | 1 | 36 | 109 | 436 | 26 | 10 | 18 | 4 | 2 | 61 | 32 | 26 | 60 | 108 | 16 | 30 | 0 | 1 | 8 | 0 | 4 | 0 | 18 | 32 | 66 | 9 | 5 | 68 | 57 | 17 | 6 | 22 | 11 | 22 | 23 | 7 | 57 | 11 | 13 | 268 | 37 | 1 | 7 | 4 | 8 | 19 | 44 | 3 | 22 | 5 | 12 | 0 | 157 | 436 | 0 | 0 | 19 | 85 | 10 | 3 | 0 | 35 | 12 | 2 | 14 | 14 | 6 | 467 | 12 | 180 | 57 | 2 | 94 | 54 | 53 | 37 | 54 | 4 | 26 | 312 | 4 | 7 | 104 | 0 | 16 | 16 | 15 | 2 | 0 | 1 | 65 | 0 | 1 | 7 | 61 | 14 | 164 | 26 | 20 | 48 | 18 | 125 | 0 | 3 | 40 | 5 | 8 | 8 | 2 | 7 | 8 | 14 | 0 | 287 | 16 | 14 | 0 | 32 | 0 | 0 | 58 | 76 | 0 | 70 | 201 | 77 | 215 | 55 | 8 | 0 | 22 | 11 | 52 | 29 | 0 | 30 | 2 | 12 | 35 | 276 | 10 | 39 | 4 | 46 | 144 | 213 | 11 | 62 | 50 | 2 | 16 | 23 | 0 | 18 | 2 | 23 | 2 | 21 | 0 | 516 | 83 | 42 | 4 | 26 | 11 | 23 | 1 | 1 | 19 | 20 | 6 | 23 | 1 | 6 | 12 | 3 | 4 | 23 | 17 | 85 | 9 | 3 | 23 | 5 | 0 | 5 | 12 | 10 | 4 | 2 | 32 | 26 | 9 | 49 | 454 | 21 | 11 | 41 | 0 | 1 | 0 | 62 | 7 | 0 | 53 | 0 | 9 | 2 | 3 | 193 | 2 | 119 | 82 | 71 | 15 | 178 | 8 | 15 | 82 | 168 | 77 | 66 | 53 | 41 | 26 | 8 | 67 | 21 | 3 |
3 | cajun_creole | 291 | 18 | 12 | 39 | 14 | 4 | 17 | 62 | 64 | 8 | 94 | 360 | 164 | 0 | 79 | 696 | 496 | 23 | 152 | 133 | 176 | 3 | 348 | 111 | 498 | 33 | 15 | 84 | 30 | 1 | 53 | 399 | 619 | 20 | 174 | 9 | 741 | 3 | 28 | 142 | 0 | 5 | 3 | 22 | 11 | 544 | 19 | 26 | 46 | 329 | 29 | 9 | 10 | 17 | 26 | 299 | 10 | 145 | 23 | 24 | 206 | 49 | 5 | 89 | 0 | 57 | 1 | 14 | 277 | 33 | 13 | 5 | 18 | 646 | 237 | 4 | 0 | 47 | 54 | 51 | 9 | 1 | 75 | 41 | 4 | 12 | 69 | 50 | 450 | 31 | 546 | 52 | 0 | 1003 | 6 | 6 | 51 | 58 | 0 | 913 | 679 | 61 | 94 | 74 | 0 | 14 | 286 | 15 | 45 | 16 | 50 | 196 | 3 | 27 | 23 | 108 | 0 | 223 | 158 | 13 | 297 | 7 | 253 | 24 | 40 | 42 | 23 | 76 | 17 | 0 | 80 | 73 | 109 | 2 | 165 | 121 | 3 | 0 | 46 | 8 | 11 | 83 | 133 | 6 | 26 | 821 | 402 | 1314 | 36 | 225 | 30 | 262 | 58 | 369 | 99 | 27 | 32 | 31 | 40 | 39 | 2326 | 17 | 6 | 26 | 61 | 58 | 450 | 38 | 9 | 555 | 36 | 183 | 537 | 1 | 33 | 0 | 27 | 2 | 16 | 2 | 993 | 665 | 463 | 97 | 36 | 631 | 34 | 4 | 1 | 23 | 7 | 15 | 8 | 1 | 6 | 25 | 473 | 125 | 100 | 198 | 26 | 107 | 35 | 25 | 14 | 5 | 18 | 75 | 26 | 2 | 8 | 28 | 16 | 6 | 148 | 310 | 76 | 7 | 18 | 1 | 0 | 55 | 428 | 8 | 1 | 673 | 6 | 1 | 62 | 1 | 119 | 3 | 74 | 313 | 72 | 24 | 340 | 16 | 5 | 41 | 371 | 77 | 105 | 195 | 56 | 146 | 3 | 34 | 13 | 13 |
4 | chinese | 129 | 0 | 46 | 36 | 26 | 9 | 92 | 26 | 100 | 32 | 34 | 14 | 330 | 109 | 167 | 261 | 529 | 56 | 391 | 15 | 460 | 171 | 368 | 363 | 112 | 2 | 251 | 187 | 0 | 5 | 379 | 36 | 113 | 2 | 33 | 12 | 1330 | 1 | 147 | 588 | 604 | 0 | 1 | 63 | 4 | 217 | 47 | 245 | 65 | 497 | 44 | 44 | 91 | 9 | 9 | 355 | 50 | 1005 | 2 | 12 | 54 | 6 | 2 | 157 | 61 | 16 | 23 | 364 | 21 | 8 | 1 | 8 | 9 | 395 | 697 | 37 | 0 | 12 | 25 | 56 | 16 | 1 | 45 | 22 | 112 | 89 | 237 | 2 | 375 | 54 | 1048 | 95 | 1 | 1687 | 1483 | 7 | 52 | 29 | 1 | 987 | 788 | 73 | 30 | 6 | 326 | 257 | 109 | 11 | 3 | 0 | 20 | 222 | 0 | 9 | 131 | 187 | 11 | 256 | 16 | 42 | 155 | 29 | 106 | 43 | 91 | 298 | 79 | 304 | 1 | 1 | 12 | 47 | 62 | 1 | 74 | 325 | 14 | 27 | 33 | 0 | 1 | 357 | 39 | 320 | 10 | 3005 | 151 | 1300 | 192 | 2 | 352 | 14 | 4 | 17 | 227 | 7 | 246 | 473 | 0 | 202 | 1733 | 177 | 16 | 35 | 557 | 43 | 464 | 33 | 5 | 758 | 106 | 66 | 1249 | 1 | 131 | 141 | 1 | 0 | 3 | 1 | 1237 | 3450 | 30 | 608 | 69 | 38 | 325 | 15 | 1488 | 73 | 1 | 7 | 197 | 204 | 30 | 73 | 251 | 329 | 137 | 5 | 53 | 457 | 14 | 16 | 2244 | 24 | 36 | 52 | 15 | 171 | 6 | 952 | 131 | 50 | 229 | 1419 | 84 | 9 | 25 | 1 | 39 | 87 | 10 | 249 | 177 | 111 | 12 | 0 | 23 | 6 | 48 | 8 | 22 | 724 | 870 | 34 | 1129 | 10 | 24 | 6 | 867 | 28 | 693 | 39 | 33 | 83 | 2 | 21 | 33 | 32 |
5 | filipino | 33 | 1 | 2 | 17 | 24 | 3 | 5 | 4 | 30 | 3 | 2 | 122 | 70 | 10 | 97 | 70 | 208 | 6 | 37 | 21 | 40 | 9 | 64 | 90 | 88 | 0 | 67 | 27 | 0 | 1 | 134 | 8 | 36 | 15 | 35 | 2 | 247 | 1 | 36 | 63 | 18 | 0 | 3 | 2 | 4 | 35 | 25 | 18 | 8 | 94 | 5 | 149 | 5 | 42 | 4 | 122 | 5 | 80 | 0 | 1 | 57 | 13 | 0 | 21 | 5 | 8 | 5 | 12 | 7 | 1 | 0 | 1 | 1 | 37 | 181 | 27 | 0 | 5 | 30 | 3 | 1 | 0 | 14 | 7 | 6 | 108 | 24 | 2 | 99 | 2 | 77 | 22 | 0 | 504 | 110 | 3 | 25 | 14 | 0 | 169 | 269 | 6 | 7 | 4 | 10 | 13 | 20 | 14 | 2 | 0 | 12 | 58 | 0 | 4 | 24 | 21 | 2 | 29 | 28 | 5 | 140 | 3 | 61 | 1 | 4 | 12 | 28 | 18 | 0 | 0 | 12 | 23 | 6 | 0 | 191 | 44 | 1 | 0 | 1 | 0 | 1 | 21 | 6 | 48 | 1 | 476 | 57 | 466 | 10 | 5 | 33 | 11 | 2 | 11 | 30 | 6 | 32 | 18 | 0 | 6 | 511 | 90 | 3 | 4 | 212 | 65 | 90 | 13 | 26 | 72 | 1 | 15 | 128 | 0 | 3 | 11 | 0 | 3 | 0 | 1 | 483 | 511 | 14 | 18 | 15 | 15 | 17 | 1 | 30 | 20 | 1 | 2 | 4 | 10 | 19 | 19 | 74 | 24 | 12 | 5 | 4 | 21 | 2 | 4 | 290 | 3 | 27 | 4 | 0 | 32 | 6 | 57 | 9 | 7 | 31 | 319 | 41 | 28 | 3 | 1 | 29 | 31 | 6 | 1 | 13 | 122 | 2 | 1 | 1 | 5 | 7 | 5 | 37 | 108 | 172 | 9 | 348 | 4 | 1 | 1 | 156 | 25 | 17 | 9 | 17 | 35 | 0 | 21 | 3 | 6 |
6 | french | 594 | 38 | 176 | 16 | 127 | 3 | 42 | 145 | 129 | 35 | 159 | 303 | 246 | 0 | 172 | 112 | 725 | 52 | 80 | 265 | 109 | 13 | 305 | 113 | 1162 | 15 | 20 | 28 | 93 | 10 | 255 | 61 | 162 | 24 | 554 | 71 | 536 | 14 | 8 | 11 | 3 | 14 | 1 | 110 | 237 | 439 | 33 | 14 | 106 | 538 | 38 | 23 | 58 | 17 | 91 | 231 | 21 | 137 | 10 | 28 | 699 | 81 | 13 | 26 | 9 | 18 | 13 | 48 | 60 | 190 | 19 | 44 | 12 | 724 | 1194 | 42 | 0 | 256 | 292 | 171 | 101 | 7 | 150 | 100 | 17 | 41 | 24 | 127 | 803 | 90 | 1457 | 93 | 0 | 777 | 44 | 45 | 124 | 287 | 5 | 229 | 866 | 69 | 75 | 247 | 1 | 72 | 42 | 98 | 40 | 7 | 1 | 459 | 54 | 5 | 2 | 159 | 34 | 863 | 283 | 5 | 420 | 139 | 527 | 59 | 59 | 93 | 39 | 76 | 77 | 0 | 47 | 21 | 17 | 0 | 484 | 62 | 55 | 1 | 26 | 6 | 11 | 234 | 255 | 12 | 140 | 1026 | 945 | 754 | 237 | 48 | 9 | 38 | 115 | 491 | 98 | 11 | 29 | 8 | 23 | 92 | 1215 | 111 | 22 | 75 | 75 | 299 | 296 | 65 | 24 | 428 | 61 | 83 | 34 | 15 | 61 | 18 | 111 | 50 | 39 | 0 | 1644 | 96 | 31 | 18 | 211 | 21 | 84 | 2 | 4 | 322 | 7 | 31 | 76 | 22 | 24 | 52 | 48 | 56 | 143 | 32 | 18 | 121 | 22 | 56 | 7 | 4 | 38 | 203 | 189 | 3 | 30 | 93 | 61 | 21 | 122 | 1156 | 32 | 25 | 60 | 0 | 0 | 42 | 534 | 33 | 5 | 427 | 1 | 1 | 22 | 2 | 597 | 62 | 403 | 169 | 327 | 45 | 661 | 12 | 25 | 248 | 830 | 198 | 663 | 20 | 76 | 94 | 23 | 347 | 101 | 90 |
7 | greek | 100 | 32 | 29 | 15 | 5 | 6 | 34 | 3 | 69 | 26 | 76 | 38 | 67 | 0 | 55 | 121 | 398 | 8 | 69 | 124 | 101 | 3 | 70 | 14 | 167 | 3 | 2 | 12 | 30 | 2 | 32 | 29 | 32 | 7 | 578 | 55 | 229 | 32 | 5 | 14 | 0 | 9 | 0 | 14 | 8 | 272 | 6 | 10 | 125 | 323 | 24 | 3 | 5 | 1 | 9 | 106 | 16 | 11 | 0 | 22 | 54 | 40 | 255 | 45 | 264 | 49 | 3 | 2 | 70 | 17 | 174 | 55 | 25 | 517 | 206 | 64 | 0 | 229 | 32 | 46 | 21 | 448 | 21 | 32 | 1 | 0 | 44 | 66 | 136 | 35 | 891 | 43 | 0 | 630 | 7 | 7 | 17 | 113 | 216 | 164 | 650 | 35 | 2 | 7 | 0 | 72 | 16 | 0 | 16 | 2 | 2 | 446 | 154 | 1 | 1 | 88 | 136 | 149 | 91 | 20 | 142 | 8 | 644 | 12 | 82 | 14 | 6 | 24 | 38 | 0 | 16 | 7 | 12 | 0 | 89 | 82 | 168 | 0 | 13 | 1 | 13 | 26 | 27 | 3 | 48 | 825 | 916 | 563 | 46 | 403 | 0 | 32 | 49 | 258 | 39 | 41 | 12 | 1 | 8 | 28 | 865 | 6 | 151 | 52 | 21 | 94 | 99 | 186 | 12 | 301 | 6 | 12 | 64 | 11 | 29 | 2 | 63 | 2 | 3 | 0 | 747 | 62 | 3 | 21 | 39 | 65 | 35 | 1 | 12 | 29 | 2 | 6 | 6 | 0 | 16 | 14 | 40 | 66 | 58 | 8 | 17 | 24 | 4 | 24 | 6 | 6 | 110 | 93 | 27 | 4 | 7 | 10 | 20 | 32 | 24 | 146 | 16 | 2 | 4 | 0 | 0 | 11 | 71 | 6 | 0 | 496 | 5 | 0 | 14 | 5 | 62 | 1 | 35 | 55 | 172 | 16 | 185 | 19 | 32 | 3 | 175 | 56 | 199 | 3 | 15 | 45 | 280 | 28 | 70 | 49 |
8 | indian | 150 | 24 | 124 | 8 | 45 | 5 | 71 | 0 | 110 | 6 | 15 | 231 | 119 | 1 | 58 | 150 | 688 | 43 | 253 | 80 | 257 | 19 | 192 | 220 | 429 | 21 | 37 | 161 | 0 | 588 | 213 | 374 | 34 | 4 | 62 | 13 | 814 | 197 | 376 | 1544 | 2 | 3 | 2 | 7 | 5 | 763 | 28 | 1044 | 545 | 904 | 49 | 632 | 19 | 17 | 5 | 209 | 1155 | 90 | 1 | 22 | 286 | 27 | 3 | 137 | 97 | 1563 | 862 | 14 | 157 | 7 | 6 | 16 | 3 | 228 | 133 | 62 | 0 | 53 | 19 | 115 | 151 | 5 | 69 | 79 | 35 | 27 | 137 | 8 | 531 | 70 | 1665 | 170 | 864 | 1635 | 1475 | 52 | 19 | 95 | 141 | 931 | 2792 | 54 | 0 | 108 | 0 | 63 | 79 | 29 | 5 | 1 | 127 | 599 | 0 | 10 | 24 | 257 | 111 | 119 | 136 | 3 | 962 | 6 | 588 | 31 | 12 | 79 | 272 | 50 | 88 | 998 | 13 | 34 | 24 | 0 | 636 | 154 | 223 | 0 | 24 | 1 | 4 | 43 | 526 | 8 | 88 | 2031 | 424 | 1844 | 23 | 3 | 2 | 243 | 4 | 47 | 619 | 3 | 288 | 99 | 2 | 187 | 1648 | 162 | 414 | 74 | 26 | 480 | 1523 | 170 | 117 | 885 | 20 | 14 | 593 | 5 | 54 | 149 | 1 | 115 | 0 | 2 | 2402 | 141 | 1 | 36 | 116 | 16 | 1739 | 136 | 50 | 56 | 0 | 3 | 4 | 0 | 30 | 29 | 75 | 225 | 58 | 34 | 47 | 66 | 3 | 28 | 35 | 0 | 183 | 79 | 38 | 14 | 29 | 44 | 17 | 260 | 106 | 533 | 95 | 23 | 7 | 0 | 20 | 132 | 20 | 17 | 41 | 1259 | 8 | 509 | 11 | 732 | 89 | 45 | 18 | 744 | 153 | 60 | 967 | 49 | 85 | 23 | 247 | 187 | 55 | 2 | 73 | 221 | 576 | 3 | 22 | 43 |
9 | irish | 219 | 20 | 13 | 15 | 31 | 2 | 11 | 63 | 255 | 6 | 3 | 51 | 10 | 0 | 120 | 11 | 142 | 14 | 4 | 37 | 5 | 0 | 61 | 75 | 334 | 96 | 88 | 4 | 4 | 5 | 121 | 11 | 42 | 39 | 83 | 12 | 66 | 0 | 2 | 3 | 0 | 11 | 1 | 20 | 37 | 86 | 16 | 5 | 43 | 59 | 9 | 4 | 9 | 7 | 16 | 69 | 2 | 43 | 0 | 6 | 174 | 11 | 2 | 7 | 5 | 3 | 0 | 19 | 11 | 14 | 7 | 6 | 3 | 110 | 215 | 0 | 0 | 12 | 43 | 35 | 7 | 0 | 8 | 15 | 6 | 1 | 4 | 9 | 341 | 23 | 181 | 22 | 0 | 107 | 26 | 14 | 41 | 44 | 2 | 71 | 222 | 5 | 15 | 46 | 0 | 12 | 8 | 10 | 5 | 0 | 3 | 50 | 0 | 0 | 2 | 33 | 41 | 107 | 31 | 3 | 54 | 41 | 61 | 8 | 3 | 21 | 7 | 10 | 44 | 0 | 8 | 17 | 0 | 0 | 186 | 8 | 10 | 0 | 20 | 0 | 3 | 26 | 44 | 0 | 39 | 138 | 65 | 238 | 45 | 4 | 3 | 16 | 7 | 83 | 23 | 1 | 23 | 5 | 7 | 16 | 284 | 14 | 11 | 3 | 25 | 274 | 165 | 3 | 50 | 71 | 10 | 13 | 2 | 0 | 11 | 2 | 19 | 0 | 11 | 1 | 462 | 42 | 31 | 14 | 17 | 9 | 42 | 0 | 4 | 11 | 12 | 2 | 8 | 2 | 13 | 23 | 0 | 3 | 16 | 12 | 122 | 18 | 1 | 32 | 7 | 0 | 7 | 62 | 13 | 7 | 1 | 13 | 7 | 6 | 49 | 306 | 12 | 8 | 11 | 0 | 0 | 0 | 69 | 5 | 0 | 44 | 1 | 1 | 4 | 0 | 87 | 10 | 51 | 71 | 41 | 7 | 121 | 4 | 55 | 36 | 101 | 69 | 30 | 23 | 6 | 27 | 12 | 18 | 20 | 4 |
10 | italian | 919 | 12 | 266 | 168 | 34 | 25 | 237 | 231 | 305 | 372 | 2005 | 274 | 482 | 1 | 583 | 725 | 2473 | 82 | 406 | 880 | 550 | 192 | 1009 | 139 | 1660 | 48 | 48 | 45 | 311 | 9 | 434 | 65 | 428 | 116 | 5843 | 224 | 1835 | 41 | 41 | 107 | 3 | 28 | 6 | 128 | 210 | 1576 | 25 | 52 | 126 | 1965 | 156 | 23 | 64 | 51 | 95 | 819 | 20 | 218 | 78 | 117 | 994 | 399 | 133 | 878 | 40 | 37 | 9 | 37 | 513 | 107 | 24 | 223 | 130 | 2913 | 1793 | 228 | 0 | 1362 | 325 | 380 | 253 | 108 | 216 | 264 | 16 | 31 | 535 | 597 | 1361 | 247 | 4992 | 339 | 1 | 4173 | 42 | 70 | 114 | 2077 | 9 | 756 | 2981 | 237 | 97 | 377 | 0 | 131 | 249 | 56 | 1127 | 38 | 20 | 970 | 156 | 31 | 14 | 666 | 36 | 1291 | 729 | 163 | 1223 | 106 | 1401 | 177 | 74 | 82 | 50 | 326 | 189 | 1 | 63 | 77 | 92 | 2 | 737 | 388 | 169 | 0 | 97 | 22 | 1249 | 843 | 147 | 314 | 237 | 5183 | 4987 | 2717 | 289 | 987 | 9 | 89 | 2456 | 1746 | 450 | 1015 | 207 | 5 | 43 | 267 | 5668 | 38 | 34 | 380 | 231 | 286 | 723 | 351 | 85 | 2053 | 123 | 171 | 367 | 665 | 234 | 6 | 508 | 37 | 277 | 13 | 4897 | 1341 | 588 | 63 | 340 | 558 | 179 | 7 | 17 | 316 | 38 | 128 | 78 | 66 | 25 | 591 | 248 | 347 | 439 | 64 | 72 | 415 | 80 | 112 | 23 | 357 | 574 | 647 | 163 | 5 | 146 | 86 | 97 | 30 | 270 | 1378 | 199 | 16 | 35 | 0 | 2 | 65 | 524 | 63 | 17 | 3346 | 17 | 6 | 186 | 2 | 580 | 93 | 326 | 541 | 774 | 183 | 1483 | 63 | 173 | 279 | 1710 | 451 | 1521 | 60 | 248 | 366 | 34 | 226 | 272 | 340 |
11 | jamaican | 42 | 195 | 3 | 6 | 8 | 6 | 7 | 10 | 49 | 1 | 8 | 21 | 60 | 0 | 59 | 74 | 222 | 6 | 35 | 35 | 41 | 1 | 29 | 109 | 92 | 2 | 10 | 10 | 1 | 2 | 49 | 50 | 16 | 2 | 17 | 4 | 178 | 0 | 59 | 57 | 5 | 4 | 3 | 7 | 4 | 70 | 17 | 34 | 127 | 145 | 6 | 140 | 32 | 10 | 1 | 46 | 20 | 24 | 13 | 5 | 24 | 31 | 0 | 16 | 1 | 30 | 76 | 44 | 16 | 6 | 2 | 7 | 1 | 163 | 65 | 1 | 0 | 6 | 23 | 11 | 0 | 0 | 11 | 11 | 0 | 13 | 34 | 3 | 111 | 5 | 226 | 6 | 2 | 313 | 124 | 1 | 12 | 32 | 1 | 109 | 542 | 13 | 5 | 2 | 0 | 8 | 63 | 10 | 1 | 0 | 30 | 136 | 0 | 1 | 33 | 33 | 1 | 24 | 9 | 9 | 59 | 1 | 23 | 1 | 6 | 26 | 153 | 17 | 2 | 3 | 9 | 17 | 4 | 0 | 146 | 31 | 2 | 0 | 9 | 0 | 4 | 5 | 17 | 3 | 120 | 270 | 73 | 374 | 47 | 7 | 1 | 28 | 2 | 15 | 16 | 1 | 19 | 8 | 8 | 14 | 616 | 10 | 11 | 5 | 32 | 53 | 202 | 26 | 11 | 123 | 2 | 3 | 67 | 0 | 9 | 16 | 6 | 0 | 8 | 2 | 419 | 173 | 0 | 113 | 21 | 52 | 19 | 5 | 2 | 9 | 0 | 1 | 4 | 0 | 1 | 7 | 20 | 32 | 15 | 6 | 14 | 17 | 2 | 9 | 81 | 0 | 14 | 16 | 11 | 8 | 6 | 11 | 9 | 7 | 38 | 204 | 27 | 12 | 8 | 0 | 0 | 17 | 296 | 3 | 0 | 85 | 5 | 15 | 3 | 13 | 19 | 16 | 37 | 121 | 90 | 5 | 210 | 5 | 2 | 3 | 91 | 10 | 24 | 10 | 12 | 35 | 2 | 2 | 7 | 5 |
12 | japanese | 69 | 1 | 13 | 5 | 18 | 60 | 38 | 8 | 55 | 4 | 10 | 24 | 51 | 22 | 71 | 51 | 154 | 19 | 91 | 14 | 65 | 17 | 67 | 115 | 105 | 0 | 105 | 63 | 0 | 38 | 204 | 26 | 19 | 1 | 35 | 9 | 276 | 3 | 28 | 182 | 17 | 1 | 1 | 37 | 8 | 74 | 8 | 93 | 23 | 116 | 9 | 43 | 32 | 8 | 4 | 72 | 65 | 114 | 0 | 7 | 61 | 4 | 0 | 22 | 116 | 65 | 36 | 54 | 6 | 13 | 5 | 6 | 7 | 128 | 334 | 39 | 0 | 22 | 23 | 12 | 12 | 0 | 103 | 21 | 68 | 27 | 120 | 6 | 203 | 15 | 404 | 44 | 46 | 358 | 461 | 3 | 48 | 19 | 1 | 330 | 261 | 8 | 4 | 15 | 9 | 79 | 40 | 14 | 2 | 1 | 5 | 143 | 0 | 5 | 37 | 81 | 0 | 124 | 18 | 2 | 111 | 19 | 140 | 3 | 11 | 48 | 64 | 79 | 1 | 51 | 51 | 28 | 19 | 0 | 102 | 49 | 5 | 402 | 15 | 0 | 4 | 147 | 48 | 136 | 3 | 901 | 81 | 472 | 36 | 0 | 13 | 9 | 8 | 11 | 184 | 3 | 75 | 32 | 4 | 58 | 390 | 10 | 16 | 8 | 112 | 96 | 236 | 14 | 4 | 179 | 34 | 13 | 554 | 1 | 17 | 51 | 0 | 7 | 0 | 0 | 581 | 940 | 2 | 236 | 75 | 24 | 346 | 2 | 520 | 30 | 1 | 8 | 15 | 138 | 9 | 28 | 71 | 65 | 41 | 22 | 20 | 114 | 12 | 3 | 779 | 2 | 73 | 27 | 4 | 42 | 10 | 111 | 50 | 10 | 64 | 650 | 41 | 10 | 10 | 0 | 7 | 65 | 3 | 137 | 127 | 78 | 1 | 29 | 3 | 34 | 21 | 0 | 20 | 292 | 337 | 11 | 479 | 20 | 16 | 2 | 300 | 25 | 65 | 22 | 7 | 48 | 4 | 49 | 14 | 18 |
13 | korean | 31 | 0 | 1 | 0 | 27 | 2 | 22 | 8 | 10 | 4 | 2 | 3 | 64 | 32 | 166 | 31 | 192 | 4 | 26 | 3 | 10 | 4 | 37 | 168 | 15 | 2 | 110 | 46 | 0 | 0 | 180 | 16 | 5 | 6 | 13 | 0 | 109 | 1 | 77 | 162 | 20 | 2 | 0 | 21 | 0 | 45 | 12 | 17 | 6 | 181 | 16 | 8 | 20 | 0 | 0 | 73 | 6 | 66 | 0 | 3 | 6 | 1 | 0 | 47 | 81 | 1 | 2 | 69 | 1 | 3 | 0 | 1 | 3 | 45 | 170 | 4 | 0 | 4 | 6 | 4 | 0 | 0 | 8 | 2 | 35 | 59 | 149 | 4 | 88 | 4 | 186 | 3 | 0 | 639 | 320 | 4 | 24 | 1 | 1 | 348 | 191 | 1 | 3 | 3 | 4 | 80 | 40 | 7 | 0 | 1 | 16 | 63 | 0 | 1 | 13 | 74 | 6 | 52 | 10 | 11 | 39 | 9 | 20 | 2 | 46 | 36 | 11 | 50 | 0 | 0 | 9 | 17 | 10 | 0 | 6 | 112 | 0 | 59 | 7 | 0 | 1 | 89 | 13 | 56 | 0 | 815 | 42 | 625 | 8 | 0 | 20 | 11 | 0 | 3 | 96 | 2 | 9 | 19 | 0 | 21 | 578 | 2 | 4 | 2 | 90 | 52 | 64 | 8 | 2 | 237 | 22 | 91 | 414 | 0 | 24 | 16 | 1 | 0 | 0 | 1 | 382 | 676 | 4 | 197 | 36 | 10 | 368 | 4 | 897 | 7 | 1 | 1 | 12 | 77 | 9 | 20 | 31 | 11 | 24 | 3 | 9 | 84 | 0 | 3 | 544 | 1 | 65 | 15 | 0 | 46 | 4 | 47 | 85 | 4 | 33 | 488 | 44 | 0 | 46 | 0 | 5 | 13 | 1 | 191 | 67 | 7 | 10 | 0 | 5 | 0 | 4 | 0 | 4 | 135 | 216 | 4 | 278 | 0 | 5 | 0 | 148 | 5 | 84 | 1 | 2 | 40 | 1 | 10 | 3 | 54 |
14 | mexican | 330 | 66 | 83 | 86 | 92 | 1103 | 43 | 126 | 256 | 24 | 63 | 188 | 1710 | 0 | 839 | 928 | 2245 | 40 | 608 | 109 | 853 | 16 | 858 | 263 | 577 | 30 | 192 | 267 | 27 | 2 | 169 | 425 | 111 | 1135 | 2976 | 91 | 2473 | 16 | 1490 | 3039 | 0 | 444 | 505 | 24 | 129 | 2077 | 144 | 2794 | 319 | 1168 | 169 | 54 | 37 | 138 | 20 | 719 | 218 | 2152 | 42 | 49 | 1910 | 46 | 39 | 155 | 68 | 2061 | 6 | 47 | 806 | 19 | 16 | 48 | 124 | 835 | 654 | 4 | 400 | 206 | 104 | 292 | 6 | 42 | 115 | 144 | 18 | 24 | 134 | 26 | 1425 | 121 | 2684 | 356 | 0 | 3222 | 32 | 20 | 77 | 164 | 66 | 1946 | 3495 | 180 | 27 | 71 | 1 | 120 | 346 | 82 | 41 | 854 | 1175 | 1628 | 4 | 420 | 37 | 617 | 11 | 399 | 113 | 212 | 579 | 3 | 237 | 75 | 509 | 118 | 2286 | 195 | 111 | 0 | 166 | 124 | 58 | 500 | 449 | 274 | 50 | 1 | 343 | 530 | 95 | 151 | 50 | 24 | 15 | 2978 | 1846 | 4436 | 283 | 906 | 4 | 363 | 51 | 147 | 181 | 79 | 32 | 46 | 27 | 66 | 4689 | 55 | 71 | 190 | 396 | 230 | 2376 | 618 | 73 | 1175 | 189 | 48 | 520 | 16 | 247 | 4 | 16 | 7 | 7 | 1283 | 3920 | 1508 | 96 | 172 | 199 | 670 | 284 | 236 | 60 | 46 | 167 | 123 | 18 | 11 | 119 | 1456 | 178 | 487 | 328 | 168 | 38 | 293 | 206 | 1323 | 61 | 17 | 109 | 361 | 112 | 15 | 65 | 59 | 170 | 70 | 248 | 690 | 226 | 51 | 21 | 637 | 0 | 93 | 68 | 24 | 13 | 2868 | 2535 | 5 | 181 | 10 | 160 | 67 | 150 | 997 | 435 | 56 | 1099 | 256 | 114 | 72 | 1009 | 357 | 160 | 67 | 33 | 446 | 115 | 44 | 69 | 147 |
15 | moroccan | 44 | 44 | 111 | 6 | 7 | 2 | 12 | 0 | 11 | 5 | 9 | 23 | 58 | 0 | 68 | 77 | 278 | 18 | 56 | 20 | 65 | 4 | 180 | 23 | 110 | 0 | 4 | 20 | 8 | 29 | 167 | 142 | 36 | 2 | 24 | 12 | 365 | 132 | 23 | 43 | 0 | 2 | 0 | 2 | 0 | 335 | 4 | 286 | 413 | 299 | 23 | 7 | 1 | 1 | 9 | 49 | 252 | 9 | 3 | 8 | 6 | 7 | 7 | 36 | 8 | 452 | 23 | 1 | 65 | 3 | 6 | 7 | 1 | 182 | 74 | 35 | 0 | 128 | 3 | 40 | 25 | 12 | 27 | 25 | 1 | 6 | 34 | 66 | 90 | 28 | 676 | 9 | 6 | 450 | 287 | 50 | 9 | 46 | 20 | 142 | 1370 | 43 | 0 | 1 | 0 | 117 | 21 | 2 | 6 | 0 | 2 | 259 | 23 | 0 | 2 | 95 | 170 | 63 | 79 | 3 | 96 | 4 | 449 | 29 | 5 | 4 | 20 | 41 | 5 | 6 | 7 | 14 | 2 | 0 | 20 | 34 | 118 | 0 | 8 | 0 | 1 | 6 | 9 | 5 | 32 | 638 | 658 | 496 | 89 | 12 | 1 | 243 | 3 | 242 | 80 | 5 | 15 | 4 | 0 | 41 | 685 | 8 | 17 | 23 | 6 | 78 | 77 | 67 | 97 | 186 | 7 | 17 | 19 | 2 | 18 | 8 | 6 | 126 | 0 | 1 | 601 | 25 | 11 | 5 | 42 | 6 | 140 | 2 | 20 | 30 | 0 | 5 | 10 | 0 | 33 | 1 | 8 | 51 | 32 | 31 | 0 | 59 | 1 | 4 | 4 | 2 | 12 | 43 | 29 | 2 | 44 | 0 | 13 | 72 | 103 | 130 | 104 | 0 | 3 | 0 | 0 | 52 | 22 | 14 | 1 | 308 | 0 | 112 | 8 | 73 | 47 | 1 | 6 | 118 | 41 | 20 | 253 | 17 | 12 | 2 | 66 | 28 | 51 | 0 | 20 | 66 | 34 | 8 | 32 | 40 |
16 | russian | 123 | 8 | 26 | 4 | 31 | 2 | 0 | 11 | 74 | 4 | 6 | 39 | 18 | 0 | 72 | 18 | 106 | 14 | 7 | 41 | 13 | 0 | 42 | 15 | 213 | 12 | 70 | 9 | 10 | 6 | 80 | 9 | 40 | 4 | 68 | 9 | 43 | 1 | 2 | 9 | 0 | 1 | 0 | 14 | 17 | 59 | 14 | 9 | 34 | 69 | 11 | 1 | 19 | 10 | 13 | 15 | 6 | 16 | 1 | 4 | 183 | 24 | 0 | 1 | 20 | 5 | 0 | 10 | 11 | 9 | 111 | 4 | 2 | 91 | 280 | 2 | 0 | 6 | 44 | 4 | 6 | 2 | 17 | 13 | 0 | 4 | 6 | 11 | 237 | 0 | 160 | 10 | 0 | 88 | 9 | 16 | 19 | 10 | 2 | 51 | 159 | 6 | 13 | 18 | 0 | 22 | 17 | 4 | 2 | 0 | 2 | 64 | 1 | 0 | 9 | 23 | 3 | 121 | 23 | 8 | 39 | 9 | 101 | 0 | 2 | 8 | 0 | 4 | 7 | 2 | 31 | 11 | 2 | 0 | 98 | 10 | 4 | 0 | 5 | 0 | 4 | 47 | 17 | 8 | 8 | 173 | 66 | 209 | 14 | 2 | 0 | 20 | 2 | 59 | 19 | 1 | 20 | 0 | 0 | 14 | 213 | 12 | 15 | 4 | 26 | 128 | 82 | 8 | 40 | 57 | 5 | 16 | 20 | 3 | 4 | 9 | 3 | 4 | 1 | 0 | 345 | 32 | 10 | 9 | 14 | 7 | 43 | 0 | 0 | 9 | 1 | 0 | 2 | 2 | 2 | 9 | 4 | 7 | 15 | 15 | 24 | 8 | 7 | 127 | 3 | 0 | 4 | 6 | 2 | 0 | 0 | 14 | 9 | 8 | 20 | 251 | 18 | 3 | 6 | 0 | 0 | 1 | 11 | 5 | 2 | 82 | 1 | 1 | 6 | 0 | 94 | 7 | 63 | 91 | 74 | 22 | 167 | 2 | 8 | 18 | 95 | 28 | 38 | 6 | 57 | 9 | 13 | 36 | 10 | 2 |
17 | southern_us | 1223 | 59 | 70 | 61 | 232 | 13 | 48 | 431 | 1240 | 31 | 82 | 192 | 157 | 0 | 86 | 303 | 866 | 65 | 130 | 258 | 145 | 13 | 390 | 627 | 2136 | 712 | 64 | 115 | 8 | 9 | 151 | 331 | 350 | 403 | 872 | 48 | 922 | 2 | 68 | 219 | 0 | 30 | 23 | 92 | 112 | 751 | 228 | 58 | 399 | 396 | 73 | 121 | 67 | 115 | 65 | 311 | 16 | 829 | 318 | 58 | 965 | 168 | 16 | 154 | 25 | 75 | 28 | 169 | 137 | 101 | 44 | 21 | 65 | 501 | 1688 | 3 | 1 | 98 | 591 | 119 | 13 | 4 | 73 | 98 | 77 | 7 | 166 | 47 | 1847 | 71 | 1026 | 168 | 2 | 987 | 116 | 41 | 289 | 231 | 14 | 789 | 1532 | 128 | 223 | 255 | 2 | 176 | 332 | 152 | 52 | 46 | 110 | 594 | 3 | 117 | 102 | 357 | 3 | 934 | 114 | 14 | 216 | 18 | 586 | 29 | 55 | 309 | 169 | 80 | 77 | 2 | 182 | 56 | 33 | 5 | 1117 | 106 | 109 | 0 | 164 | 19 | 13 | 77 | 336 | 5 | 221 | 1276 | 429 | 1365 | 205 | 55 | 28 | 270 | 136 | 286 | 36 | 18 | 210 | 175 | 429 | 37 | 2709 | 27 | 30 | 29 | 221 | 314 | 1414 | 95 | 48 | 559 | 57 | 134 | 154 | 1 | 87 | 7 | 54 | 1 | 79 | 18 | 3088 | 798 | 153 | 71 | 106 | 365 | 112 | 4 | 14 | 64 | 212 | 51 | 25 | 10 | 35 | 221 | 217 | 98 | 226 | 155 | 473 | 101 | 89 | 137 | 35 | 2 | 21 | 206 | 75 | 1 | 36 | 159 | 64 | 28 | 118 | 2319 | 316 | 125 | 271 | 2 | 0 | 49 | 259 | 29 | 0 | 469 | 9 | 9 | 49 | 12 | 660 | 69 | 778 | 658 | 459 | 31 | 895 | 27 | 47 | 243 | 837 | 328 | 161 | 182 | 40 | 377 | 37 | 146 | 102 | 14 |
18 | spanish | 53 | 4 | 94 | 5 | 24 | 23 | 16 | 24 | 33 | 24 | 35 | 93 | 64 | 0 | 36 | 265 | 251 | 19 | 33 | 117 | 42 | 2 | 133 | 22 | 92 | 3 | 9 | 7 | 20 | 1 | 45 | 40 | 24 | 7 | 88 | 14 | 262 | 23 | 48 | 68 | 0 | 5 | 6 | 16 | 6 | 224 | 8 | 86 | 68 | 381 | 16 | 4 | 5 | 16 | 5 | 66 | 11 | 41 | 4 | 8 | 68 | 35 | 5 | 56 | 78 | 70 | 5 | 8 | 74 | 12 | 5 | 6 | 1 | 275 | 274 | 13 | 0 | 255 | 35 | 40 | 17 | 4 | 32 | 23 | 1 | 16 | 44 | 82 | 98 | 32 | 487 | 46 | 0 | 561 | 11 | 13 | 10 | 39 | 1 | 258 | 361 | 21 | 53 | 18 | 0 | 25 | 60 | 26 | 18 | 1 | 31 | 243 | 8 | 9 | 6 | 122 | 7 | 251 | 130 | 4 | 104 | 11 | 214 | 25 | 13 | 8 | 67 | 37 | 17 | 0 | 15 | 12 | 32 | 1 | 107 | 62 | 25 | 0 | 11 | 1 | 2 | 24 | 16 | 2 | 16 | 734 | 729 | 516 | 141 | 67 | 4 | 196 | 12 | 245 | 42 | 6 | 72 | 0 | 3 | 30 | 910 | 8 | 10 | 52 | 44 | 166 | 53 | 55 | 28 | 407 | 10 | 14 | 135 | 2 | 58 | 0 | 23 | 126 | 5 | 13 | 677 | 77 | 89 | 24 | 52 | 9 | 18 | 33 | 1 | 29 | 1 | 5 | 127 | 3 | 5 | 2 | 121 | 31 | 55 | 75 | 18 | 56 | 2 | 19 | 4 | 5 | 19 | 49 | 27 | 0 | 3 | 17 | 6 | 34 | 58 | 188 | 60 | 16 | 7 | 0 | 0 | 31 | 73 | 4 | 0 | 436 | 8 | 9 | 18 | 3 | 44 | 3 | 47 | 81 | 214 | 8 | 206 | 29 | 4 | 17 | 247 | 34 | 270 | 14 | 12 | 64 | 11 | 41 | 33 | 19 |
19 | thai | 17 | 1 | 8 | 7 | 18 | 14 | 56 | 1 | 8 | 4 | 334 | 14 | 136 | 152 | 54 | 255 | 127 | 13 | 231 | 16 | 313 | 40 | 204 | 339 | 228 | 1 | 91 | 77 | 0 | 21 | 225 | 53 | 21 | 0 | 12 | 34 | 762 | 8 | 285 | 556 | 35 | 1 | 0 | 23 | 0 | 355 | 15 | 730 | 23 | 357 | 11 | 743 | 14 | 9 | 0 | 178 | 189 | 110 | 0 | 6 | 44 | 9 | 1 | 106 | 125 | 88 | 480 | 86 | 12 | 2 | 0 | 2 | 11 | 149 | 209 | 36 | 0 | 9 | 6 | 53 | 4 | 1 | 55 | 7 | 89 | 832 | 137 | 1 | 54 | 24 | 1325 | 28 | 2 | 957 | 521 | 10 | 30 | 33 | 2 | 575 | 389 | 55 | 0 | 8 | 17 | 80 | 66 | 10 | 2 | 1 | 84 | 590 | 0 | 4 | 30 | 84 | 3 | 147 | 18 | 11 | 527 | 7 | 151 | 21 | 77 | 184 | 1114 | 106 | 8 | 3 | 10 | 14 | 68 | 0 | 632 | 122 | 171 | 7 | 11 | 1 | 4 | 135 | 14 | 258 | 10 | 1057 | 125 | 699 | 38 | 4 | 46 | 19 | 2 | 8 | 570 | 5 | 89 | 625 | 0 | 111 | 962 | 38 | 5 | 11 | 83 | 49 | 175 | 84 | 6 | 831 | 58 | 17 | 653 | 0 | 183 | 82 | 0 | 2 | 1 | 2 | 585 | 1636 | 0 | 138 | 47 | 19 | 136 | 50 | 247 | 244 | 0 | 7 | 8 | 40 | 4 | 55 | 293 | 220 | 100 | 2 | 3 | 169 | 4 | 0 | 506 | 9 | 37 | 46 | 33 | 59 | 32 | 75 | 73 | 48 | 119 | 865 | 128 | 12 | 22 | 0 | 487 | 62 | 4 | 54 | 106 | 148 | 15 | 39 | 18 | 41 | 55 | 121 | 3 | 414 | 233 | 9 | 381 | 114 | 12 | 3 | 202 | 13 | 49 | 4 | 2 | 68 | 7 | 1 | 55 | 46 |
20 | vietnamese | 12 | 4 | 4 | 4 | 11 | 14 | 20 | 2 | 9 | 1 | 162 | 9 | 84 | 127 | 110 | 40 | 190 | 5 | 66 | 10 | 69 | 11 | 78 | 99 | 63 | 0 | 61 | 85 | 0 | 16 | 254 | 10 | 15 | 0 | 2 | 2 | 238 | 0 | 170 | 251 | 36 | 1 | 1 | 9 | 0 | 160 | 9 | 336 | 54 | 207 | 9 | 62 | 13 | 18 | 1 | 71 | 83 | 60 | 0 | 13 | 7 | 2 | 0 | 34 | 158 | 9 | 30 | 35 | 8 | 1 | 6 | 1 | 5 | 69 | 94 | 6 | 0 | 5 | 4 | 14 | 10 | 0 | 19 | 7 | 38 | 490 | 41 | 1 | 59 | 5 | 563 | 7 | 0 | 542 | 228 | 4 | 24 | 16 | 1 | 210 | 271 | 7 | 5 | 2 | 63 | 33 | 58 | 10 | 3 | 1 | 61 | 257 | 0 | 2 | 6 | 92 | 2 | 47 | 22 | 3 | 284 | 4 | 60 | 6 | 120 | 39 | 373 | 32 | 2 | 0 | 33 | 32 | 45 | 0 | 64 | 86 | 180 | 6 | 8 | 1 | 0 | 48 | 3 | 177 | 2 | 528 | 43 | 413 | 14 | 0 | 28 | 6 | 1 | 5 | 65 | 0 | 17 | 170 | 0 | 34 | 499 | 25 | 2 | 6 | 166 | 15 | 77 | 45 | 0 | 176 | 20 | 12 | 477 | 0 | 79 | 27 | 0 | 0 | 0 | 0 | 400 | 941 | 11 | 100 | 20 | 11 | 68 | 37 | 147 | 158 | 0 | 3 | 2 | 27 | 16 | 44 | 135 | 50 | 44 | 0 | 5 | 65 | 5 | 1 | 236 | 4 | 8 | 19 | 31 | 63 | 8 | 66 | 65 | 63 | 51 | 546 | 26 | 20 | 4 | 0 | 134 | 18 | 0 | 37 | 55 | 58 | 3 | 13 | 6 | 19 | 33 | 5 | 4 | 171 | 195 | 24 | 298 | 43 | 8 | 0 | 141 | 11 | 33 | 1 | 2 | 69 | 2 | 7 | 5 | 3 |
aggregate(ingredientsDTM_train[,c(1:250)], list(ingredientsDTM_train[,251]), mean)
Group.1 | all-purpos | allspic | almond | and | appl | avocado | babi | bacon | bake | balsam | basil | bay | bean | beansprout | beef | bell | black | boil | boneless | bread | breast | broccoli | broth | brown | butter | buttermilk | cabbag | canola | caper | cardamom | carrot | cayenn | celeri | cheddar | chees | cherri | chicken | chickpea | chile | chili | chines | chip | chipotl | chive | chocol | chop | cider | cilantro | cinnamon | clove | coars | coconut | cold | condens | confection | cook | coriand | corn | cornmeal | crack | cream | crumb | crumbl | crush | cucumb | cumin | curri | dark | dice | dijon | dill | dough | dress | dri | egg | eggplant | enchilada | extra-virgin | extract | fat | fennel | feta | fillet | fine | firm | fish | flake | flat | flour | free | fresh | frozen | garam | garlic | ginger | golden | granul | grate | greek | green | ground | halv | ham | heavi | hoisin | honey | hot | ice | italian | jack | jalapeno | juic | kalamata | kernel | ketchup | kosher | lamb | larg | leaf | lean | leav | leek | lemon | less | lettuc | light | lime | low | low-fat | masala | mayonais | meat | medium | mexican | milk | minc | mint | mirin | mix | monterey | mozzarella | mushroom | mustard | noodl | nutmeg | oil | oliv | onion | orang | oregano | oyster | paprika | parmesan | parsley | past | pasta | pea | peanut | pecan | peel | pepper | peppercorn | plain | plum | pork | potato | powder | purpl | raisin | red | reduc | rib | rice | ricotta | roast | root | rosemari | saffron | sage | salsa | salt | sauc | sausag | scallion | sea | season | seed | serrano | sesam | shallot | sharp | shell | sherri | shiitak | shoulder | shred | shrimp | skinless | slice | smoke | soda | sodium | soup | sour | soy | spaghetti | spinach | spray | sprig | spring | squash | starch | steak | stick | stock | sugar | sweet | sweeten | syrup | taco | thai | thigh | thyme | toast | tofu | tomato | tortilla | tumer | turkey | turmer | unsalt | unsweeten | vanilla | veget | vinegar | warm | water | wedg | wheat | whip | white | whole | wine | worcestershir | yeast | yellow | yogurt | yolk | zest | zucchini | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | brazilian | 0.0385439 | 0.002141328 | 0.05139186 | 0.004282655 | 0.02141328 | 0.02141328 | 0.006423983 | 0.06423983 | 0.04068522 | 0 | 0.004282655 | 0.1156317 | 0.1520343 | 0 | 0.06638116 | 0.1713062 | 0.2847966 | 0.00856531 | 0.03426124 | 0.02783726 | 0.03640257 | 0 | 0.07066381 | 0.04496788 | 0.1777302 | 0.004282655 | 0 | 0.02783726 | 0.002141328 | 0 | 0.05567452 | 0.04925054 | 0.01498929 | 0.01070664 | 0.1199143 | 0.01070664 | 0.1627409 | 0 | 0.04925054 | 0.08779443 | 0 | 0.00856531 | 0.01070664 | 0.004282655 | 0.07494647 | 0.1627409 | 0.004282655 | 0.1884368 | 0.02997859 | 0.1970021 | 0.01498929 | 0.3040685 | 0.02569593 | 0.1456103 | 0.002141328 | 0.05995717 | 0.04710921 | 0.07280514 | 0.00856531 | 0.006423983 | 0.07494647 | 0.01927195 | 0 | 0.06852248 | 0.004282655 | 0.0620985 | 0.004282655 | 0.01070664 | 0.04496788 | 0.00856531 | 0 | 0.002141328 | 0 | 0.1327623 | 0.2248394 | 0 | 0 | 0.02783726 | 0.0385439 | 0.01070664 | 0.006423983 | 0 | 0.05353319 | 0.01713062 | 0 | 0.05781585 | 0.07066381 | 0.02141328 | 0.1563169 | 0.01070664 | 0.2847966 | 0.06852248 | 0.002141328 | 0.4154176 | 0.06638116 | 0.002141328 | 0.04496788 | 0.04496788 | 0 | 0.1820128 | 0.2698073 | 0.006423983 | 0.03640257 | 0.01498929 | 0 | 0.02569593 | 0.04710921 | 0.1220557 | 0.01070664 | 0.006423983 | 0.04068522 | 0.2226981 | 0 | 0.00856531 | 0.002141328 | 0.04710921 | 0.004282655 | 0.07066381 | 0.07066381 | 0.002141328 | 0.1349036 | 0.004282655 | 0.07922912 | 0.006423983 | 0.002141328 | 0.03426124 | 0.3511777 | 0.02141328 | 0.01070664 | 0.002141328 | 0.02141328 | 0.006423983 | 0.02141328 | 0 | 0.490364 | 0.03211991 | 0.01498929 | 0 | 0.01284797 | 0.004282655 | 0.01070664 | 0.004282655 | 0.01070664 | 0 | 0.01070664 | 0.5289079 | 0.3104925 | 0.48394 | 0.1070664 | 0.02997859 | 0.002141328 | 0.04068522 | 0.05139186 | 0.117773 | 0.04496788 | 0.002141328 | 0.03426124 | 0.02141328 | 0.006423983 | 0.01284797 | 0.6638116 | 0.002141328 | 0.006423983 | 0.01927195 | 0.07494647 | 0.05567452 | 0.1349036 | 0.01713062 | 0.01284797 | 0.1841542 | 0.004282655 | 0.0385439 | 0.1349036 | 0 | 0.0235546 | 0.00856531 | 0.004282655 | 0.006423983 | 0.002141328 | 0.01498929 | 0.5267666 | 0.07494647 | 0.06423983 | 0.01713062 | 0.04068522 | 0.01498929 | 0.05567452 | 0.01498929 | 0.004282655 | 0.01070664 | 0.002141328 | 0.004282655 | 0.006423983 | 0 | 0.01927195 | 0.03211991 | 0.124197 | 0.02141328 | 0.04710921 | 0.03426124 | 0.01713062 | 0.03426124 | 0.004282655 | 0.01284797 | 0.006423983 | 0 | 0.01284797 | 0.006423983 | 0.002141328 | 0.00856531 | 0.004282655 | 0.04710921 | 0.02569593 | 0.00856531 | 0.0620985 | 0.3211991 | 0.0385439 | 0.130621 | 0.03426124 | 0 | 0.002141328 | 0.01284797 | 0.02783726 | 0.01070664 | 0.002141328 | 0.2847966 | 0.002141328 | 0.006423983 | 0.01070664 | 0.00856531 | 0.05567452 | 0.0620985 | 0.05995717 | 0.09850107 | 0.05567452 | 0.004282655 | 0.2398287 | 0.03211991 | 0.006423983 | 0.00856531 | 0.1948608 | 0.03211991 | 0.06423983 | 0.004282655 | 0.01284797 | 0.07280514 | 0.006423983 | 0.04710921 | 0.01713062 | 0.006423983 |
2 | british | 0.2960199 | 0.03358209 | 0.05472637 | 0.003731343 | 0.03731343 | 0 | 0.002487562 | 0.03109453 | 0.2699005 | 0.02114428 | 0.006218905 | 0.05223881 | 0.01865672 | 0 | 0.1965174 | 0.004975124 | 0.1666667 | 0.02238806 | 0.003731343 | 0.1007463 | 0.004975124 | 0.001243781 | 0.04477612 | 0.1355721 | 0.5422886 | 0.03233831 | 0.01243781 | 0.02238806 | 0.004975124 | 0.002487562 | 0.07587065 | 0.039801 | 0.03233831 | 0.07462687 | 0.1343284 | 0.0199005 | 0.03731343 | 0 | 0.001243781 | 0.009950249 | 0 | 0.004975124 | 0 | 0.02238806 | 0.039801 | 0.08208955 | 0.01119403 | 0.006218905 | 0.08457711 | 0.07089552 | 0.02114428 | 0.007462687 | 0.02736318 | 0.01368159 | 0.02736318 | 0.02860697 | 0.008706468 | 0.07089552 | 0.01368159 | 0.01616915 | 0.3333333 | 0.0460199 | 0.001243781 | 0.008706468 | 0.004975124 | 0.009950249 | 0.02363184 | 0.05472637 | 0.003731343 | 0.02736318 | 0.006218905 | 0.01492537 | 0 | 0.1952736 | 0.5422886 | 0 | 0 | 0.02363184 | 0.1057214 | 0.01243781 | 0.003731343 | 0 | 0.04353234 | 0.01492537 | 0.002487562 | 0.01741294 | 0.01741294 | 0.007462687 | 0.5808458 | 0.01492537 | 0.2238806 | 0.07089552 | 0.002487562 | 0.1169154 | 0.06716418 | 0.0659204 | 0.0460199 | 0.06716418 | 0.004975124 | 0.03233831 | 0.3880597 | 0.004975124 | 0.008706468 | 0.1293532 | 0 | 0.0199005 | 0.0199005 | 0.01865672 | 0.002487562 | 0 | 0.001243781 | 0.08084577 | 0 | 0.001243781 | 0.008706468 | 0.07587065 | 0.01741294 | 0.2039801 | 0.03233831 | 0.02487562 | 0.05970149 | 0.02238806 | 0.1554726 | 0 | 0.003731343 | 0.04975124 | 0.006218905 | 0.009950249 | 0.009950249 | 0.002487562 | 0.008706468 | 0.009950249 | 0.01741294 | 0 | 0.3569652 | 0.0199005 | 0.01741294 | 0 | 0.039801 | 0 | 0 | 0.0721393 | 0.09452736 | 0 | 0.08706468 | 0.25 | 0.09577114 | 0.2674129 | 0.06840796 | 0.009950249 | 0 | 0.02736318 | 0.01368159 | 0.06467662 | 0.03606965 | 0 | 0.03731343 | 0.002487562 | 0.01492537 | 0.04353234 | 0.3432836 | 0.01243781 | 0.04850746 | 0.004975124 | 0.05721393 | 0.1791045 | 0.2649254 | 0.01368159 | 0.07711443 | 0.06218905 | 0.002487562 | 0.0199005 | 0.02860697 | 0 | 0.02238806 | 0.002487562 | 0.02860697 | 0.002487562 | 0.0261194 | 0 | 0.641791 | 0.1032338 | 0.05223881 | 0.004975124 | 0.03233831 | 0.01368159 | 0.02860697 | 0.001243781 | 0.001243781 | 0.02363184 | 0.02487562 | 0.007462687 | 0.02860697 | 0.001243781 | 0.007462687 | 0.01492537 | 0.003731343 | 0.004975124 | 0.02860697 | 0.02114428 | 0.1057214 | 0.01119403 | 0.003731343 | 0.02860697 | 0.006218905 | 0 | 0.006218905 | 0.01492537 | 0.01243781 | 0.004975124 | 0.002487562 | 0.039801 | 0.03233831 | 0.01119403 | 0.06094527 | 0.5646766 | 0.0261194 | 0.01368159 | 0.05099502 | 0 | 0.001243781 | 0 | 0.07711443 | 0.008706468 | 0 | 0.0659204 | 0 | 0.01119403 | 0.002487562 | 0.003731343 | 0.2400498 | 0.002487562 | 0.14801 | 0.10199 | 0.08830846 | 0.01865672 | 0.221393 | 0.009950249 | 0.01865672 | 0.10199 | 0.2089552 | 0.09577114 | 0.08208955 | 0.0659204 | 0.05099502 | 0.03233831 | 0.009950249 | 0.08333333 | 0.0261194 | 0.003731343 |
3 | cajun_creole | 0.1882277 | 0.01164295 | 0.007761966 | 0.02522639 | 0.009055627 | 0.002587322 | 0.01099612 | 0.04010349 | 0.04139715 | 0.005174644 | 0.06080207 | 0.232859 | 0.1060802 | 0 | 0.05109961 | 0.450194 | 0.3208279 | 0.0148771 | 0.09831824 | 0.08602846 | 0.1138422 | 0.001940492 | 0.225097 | 0.07179819 | 0.3221216 | 0.02134541 | 0.009702458 | 0.05433376 | 0.01940492 | 0.0006468305 | 0.03428202 | 0.2580854 | 0.4003881 | 0.01293661 | 0.1125485 | 0.005821475 | 0.4793014 | 0.001940492 | 0.01811125 | 0.09184994 | 0 | 0.003234153 | 0.001940492 | 0.01423027 | 0.007115136 | 0.3518758 | 0.01228978 | 0.01681759 | 0.0297542 | 0.2128072 | 0.01875809 | 0.005821475 | 0.006468305 | 0.01099612 | 0.01681759 | 0.1934023 | 0.006468305 | 0.09379043 | 0.0148771 | 0.01552393 | 0.1332471 | 0.0316947 | 0.003234153 | 0.05756792 | 0 | 0.03686934 | 0.0006468305 | 0.009055627 | 0.1791721 | 0.02134541 | 0.008408797 | 0.003234153 | 0.01164295 | 0.4178525 | 0.1532988 | 0.002587322 | 0 | 0.03040103 | 0.03492885 | 0.03298836 | 0.005821475 | 0.0006468305 | 0.04851229 | 0.02652005 | 0.002587322 | 0.007761966 | 0.04463131 | 0.03234153 | 0.2910737 | 0.02005175 | 0.3531695 | 0.03363519 | 0 | 0.648771 | 0.003880983 | 0.003880983 | 0.03298836 | 0.03751617 | 0 | 0.5905563 | 0.4391979 | 0.03945666 | 0.06080207 | 0.04786546 | 0 | 0.009055627 | 0.1849935 | 0.009702458 | 0.02910737 | 0.01034929 | 0.03234153 | 0.1267788 | 0.001940492 | 0.01746442 | 0.0148771 | 0.0698577 | 0 | 0.1442432 | 0.1021992 | 0.008408797 | 0.1921087 | 0.004527814 | 0.1636481 | 0.01552393 | 0.02587322 | 0.02716688 | 0.0148771 | 0.04915912 | 0.01099612 | 0 | 0.05174644 | 0.04721863 | 0.07050453 | 0.001293661 | 0.106727 | 0.07826649 | 0.001940492 | 0 | 0.0297542 | 0.005174644 | 0.007115136 | 0.05368693 | 0.08602846 | 0.003880983 | 0.01681759 | 0.5310479 | 0.2600259 | 0.8499353 | 0.0232859 | 0.1455369 | 0.01940492 | 0.1694696 | 0.03751617 | 0.2386805 | 0.06403622 | 0.01746442 | 0.02069858 | 0.02005175 | 0.02587322 | 0.02522639 | 1.504528 | 0.01099612 | 0.003880983 | 0.01681759 | 0.03945666 | 0.03751617 | 0.2910737 | 0.02457956 | 0.005821475 | 0.3589909 | 0.0232859 | 0.11837 | 0.347348 | 0.0006468305 | 0.02134541 | 0 | 0.01746442 | 0.001293661 | 0.01034929 | 0.001293661 | 0.6423027 | 0.4301423 | 0.2994825 | 0.06274256 | 0.0232859 | 0.4081501 | 0.02199224 | 0.002587322 | 0.0006468305 | 0.0148771 | 0.004527814 | 0.009702458 | 0.005174644 | 0.0006468305 | 0.003880983 | 0.01617076 | 0.3059508 | 0.08085382 | 0.06468305 | 0.1280724 | 0.01681759 | 0.06921087 | 0.02263907 | 0.01617076 | 0.009055627 | 0.003234153 | 0.01164295 | 0.04851229 | 0.01681759 | 0.001293661 | 0.005174644 | 0.01811125 | 0.01034929 | 0.003880983 | 0.09573092 | 0.2005175 | 0.04915912 | 0.004527814 | 0.01164295 | 0.0006468305 | 0 | 0.03557568 | 0.2768435 | 0.005174644 | 0.0006468305 | 0.4353169 | 0.003880983 | 0.0006468305 | 0.04010349 | 0.0006468305 | 0.07697283 | 0.001940492 | 0.04786546 | 0.202458 | 0.0465718 | 0.01552393 | 0.2199224 | 0.01034929 | 0.003234153 | 0.02652005 | 0.2399741 | 0.04980595 | 0.06791721 | 0.126132 | 0.03622251 | 0.09443726 | 0.001940492 | 0.02199224 | 0.008408797 | 0.008408797 |
4 | chinese | 0.04826038 | 0 | 0.01720913 | 0.01346801 | 0.009726899 | 0.003367003 | 0.03441826 | 0.009726899 | 0.03741115 | 0.01197157 | 0.01271979 | 0.005237561 | 0.1234568 | 0.04077815 | 0.06247662 | 0.0976431 | 0.197905 | 0.02095024 | 0.1462776 | 0.005611672 | 0.1720913 | 0.06397306 | 0.137673 | 0.1358025 | 0.04190049 | 0.000748223 | 0.09390198 | 0.06995885 | 0 | 0.001870557 | 0.1417883 | 0.01346801 | 0.0422746 | 0.000748223 | 0.01234568 | 0.004489338 | 0.4975683 | 0.0003741115 | 0.05499439 | 0.2199776 | 0.2259633 | 0 | 0.0003741115 | 0.02356902 | 0.001496446 | 0.08118219 | 0.01758324 | 0.09165731 | 0.02431725 | 0.1859334 | 0.01646091 | 0.01646091 | 0.03404415 | 0.003367003 | 0.003367003 | 0.1328096 | 0.01870557 | 0.375982 | 0.000748223 | 0.004489338 | 0.02020202 | 0.002244669 | 0.000748223 | 0.0587355 | 0.0228208 | 0.005985784 | 0.008604564 | 0.1361766 | 0.007856341 | 0.002992892 | 0.0003741115 | 0.002992892 | 0.003367003 | 0.147774 | 0.2607557 | 0.01384212 | 0 | 0.004489338 | 0.009352787 | 0.02095024 | 0.005985784 | 0.0003741115 | 0.01683502 | 0.008230453 | 0.04190049 | 0.03329592 | 0.08866442 | 0.000748223 | 0.1402918 | 0.02020202 | 0.3920688 | 0.03554059 | 0.0003741115 | 0.6311261 | 0.5548073 | 0.00261878 | 0.0194538 | 0.01084923 | 0.0003741115 | 0.369248 | 0.2947999 | 0.02731014 | 0.01122334 | 0.002244669 | 0.1219603 | 0.09614665 | 0.04077815 | 0.004115226 | 0.001122334 | 0 | 0.00748223 | 0.08305275 | 0 | 0.003367003 | 0.0490086 | 0.06995885 | 0.004115226 | 0.09577254 | 0.005985784 | 0.01571268 | 0.05798728 | 0.01084923 | 0.03965582 | 0.01608679 | 0.03404415 | 0.1114852 | 0.02955481 | 0.1137299 | 0.0003741115 | 0.0003741115 | 0.004489338 | 0.01758324 | 0.02319491 | 0.0003741115 | 0.02768425 | 0.1215862 | 0.005237561 | 0.01010101 | 0.01234568 | 0 | 0.0003741115 | 0.1335578 | 0.01459035 | 0.1197157 | 0.003741115 | 1.124205 | 0.05649083 | 0.4863449 | 0.07182941 | 0.000748223 | 0.1316872 | 0.005237561 | 0.001496446 | 0.006359895 | 0.08492331 | 0.00261878 | 0.09203143 | 0.1769547 | 0 | 0.07557052 | 0.6483352 | 0.06621773 | 0.005985784 | 0.0130939 | 0.2083801 | 0.01608679 | 0.1735877 | 0.01234568 | 0.001870557 | 0.2835765 | 0.03965582 | 0.02469136 | 0.4672652 | 0.0003741115 | 0.0490086 | 0.05274972 | 0.0003741115 | 0 | 0.001122334 | 0.0003741115 | 0.4627759 | 1.290685 | 0.01122334 | 0.2274598 | 0.02581369 | 0.01421624 | 0.1215862 | 0.005611672 | 0.5566779 | 0.02731014 | 0.0003741115 | 0.00261878 | 0.07369996 | 0.07631874 | 0.01122334 | 0.02731014 | 0.09390198 | 0.1230827 | 0.05125327 | 0.001870557 | 0.01982791 | 0.1709689 | 0.005237561 | 0.005985784 | 0.8395062 | 0.008978676 | 0.01346801 | 0.0194538 | 0.005611672 | 0.06397306 | 0.002244669 | 0.3561541 | 0.0490086 | 0.01870557 | 0.08567153 | 0.5308642 | 0.03142536 | 0.003367003 | 0.009352787 | 0.0003741115 | 0.01459035 | 0.0325477 | 0.003741115 | 0.09315376 | 0.06621773 | 0.04152637 | 0.004489338 | 0 | 0.008604564 | 0.002244669 | 0.01795735 | 0.002992892 | 0.008230453 | 0.2708567 | 0.325477 | 0.01271979 | 0.4223719 | 0.003741115 | 0.008978676 | 0.002244669 | 0.3243547 | 0.01047512 | 0.2592593 | 0.01459035 | 0.01234568 | 0.03105125 | 0.000748223 | 0.007856341 | 0.01234568 | 0.01197157 |
5 | filipino | 0.04370861 | 0.001324503 | 0.002649007 | 0.02251656 | 0.03178808 | 0.00397351 | 0.006622517 | 0.005298013 | 0.0397351 | 0.00397351 | 0.002649007 | 0.1615894 | 0.09271523 | 0.01324503 | 0.1284768 | 0.09271523 | 0.2754967 | 0.00794702 | 0.04900662 | 0.02781457 | 0.05298013 | 0.01192053 | 0.08476821 | 0.1192053 | 0.1165563 | 0 | 0.08874172 | 0.03576159 | 0 | 0.001324503 | 0.1774834 | 0.01059603 | 0.04768212 | 0.01986755 | 0.04635762 | 0.002649007 | 0.3271523 | 0.001324503 | 0.04768212 | 0.08344371 | 0.02384106 | 0 | 0.00397351 | 0.002649007 | 0.005298013 | 0.04635762 | 0.03311258 | 0.02384106 | 0.01059603 | 0.1245033 | 0.006622517 | 0.197351 | 0.006622517 | 0.05562914 | 0.005298013 | 0.1615894 | 0.006622517 | 0.1059603 | 0 | 0.001324503 | 0.07549669 | 0.01721854 | 0 | 0.02781457 | 0.006622517 | 0.01059603 | 0.006622517 | 0.01589404 | 0.009271523 | 0.001324503 | 0 | 0.001324503 | 0.001324503 | 0.04900662 | 0.2397351 | 0.03576159 | 0 | 0.006622517 | 0.0397351 | 0.00397351 | 0.001324503 | 0 | 0.01854305 | 0.009271523 | 0.00794702 | 0.1430464 | 0.03178808 | 0.002649007 | 0.1311258 | 0.002649007 | 0.1019868 | 0.02913907 | 0 | 0.6675497 | 0.1456954 | 0.00397351 | 0.03311258 | 0.01854305 | 0 | 0.2238411 | 0.3562914 | 0.00794702 | 0.009271523 | 0.005298013 | 0.01324503 | 0.01721854 | 0.02649007 | 0.01854305 | 0.002649007 | 0 | 0.01589404 | 0.07682119 | 0 | 0.005298013 | 0.03178808 | 0.02781457 | 0.002649007 | 0.0384106 | 0.03708609 | 0.006622517 | 0.1854305 | 0.00397351 | 0.0807947 | 0.001324503 | 0.005298013 | 0.01589404 | 0.03708609 | 0.02384106 | 0 | 0 | 0.01589404 | 0.03046358 | 0.00794702 | 0 | 0.2529801 | 0.05827815 | 0.001324503 | 0 | 0.001324503 | 0 | 0.001324503 | 0.02781457 | 0.00794702 | 0.06357616 | 0.001324503 | 0.6304636 | 0.07549669 | 0.6172185 | 0.01324503 | 0.006622517 | 0.04370861 | 0.01456954 | 0.002649007 | 0.01456954 | 0.0397351 | 0.00794702 | 0.04238411 | 0.02384106 | 0 | 0.00794702 | 0.6768212 | 0.1192053 | 0.00397351 | 0.005298013 | 0.2807947 | 0.08609272 | 0.1192053 | 0.01721854 | 0.03443709 | 0.09536424 | 0.001324503 | 0.01986755 | 0.1695364 | 0 | 0.00397351 | 0.01456954 | 0 | 0.00397351 | 0 | 0.001324503 | 0.6397351 | 0.6768212 | 0.01854305 | 0.02384106 | 0.01986755 | 0.01986755 | 0.02251656 | 0.001324503 | 0.0397351 | 0.02649007 | 0.001324503 | 0.002649007 | 0.005298013 | 0.01324503 | 0.02516556 | 0.02516556 | 0.09801325 | 0.03178808 | 0.01589404 | 0.006622517 | 0.005298013 | 0.02781457 | 0.002649007 | 0.005298013 | 0.384106 | 0.00397351 | 0.03576159 | 0.005298013 | 0 | 0.04238411 | 0.00794702 | 0.07549669 | 0.01192053 | 0.009271523 | 0.0410596 | 0.4225166 | 0.05430464 | 0.03708609 | 0.00397351 | 0.001324503 | 0.0384106 | 0.0410596 | 0.00794702 | 0.001324503 | 0.01721854 | 0.1615894 | 0.002649007 | 0.001324503 | 0.001324503 | 0.006622517 | 0.009271523 | 0.006622517 | 0.04900662 | 0.1430464 | 0.2278146 | 0.01192053 | 0.4609272 | 0.005298013 | 0.001324503 | 0.001324503 | 0.2066225 | 0.03311258 | 0.02251656 | 0.01192053 | 0.02251656 | 0.04635762 | 0 | 0.02781457 | 0.00397351 | 0.00794702 |
6 | french | 0.2244898 | 0.0143613 | 0.0665155 | 0.006046863 | 0.04799698 | 0.001133787 | 0.01587302 | 0.0547997 | 0.04875283 | 0.01322751 | 0.0600907 | 0.1145125 | 0.09297052 | 0 | 0.06500378 | 0.04232804 | 0.2739985 | 0.01965231 | 0.03023432 | 0.1001512 | 0.04119426 | 0.004913076 | 0.1152683 | 0.04270597 | 0.4391534 | 0.005668934 | 0.007558579 | 0.01058201 | 0.03514739 | 0.003779289 | 0.09637188 | 0.02305367 | 0.06122449 | 0.009070295 | 0.2093726 | 0.02683296 | 0.2025699 | 0.005291005 | 0.003023432 | 0.004157218 | 0.001133787 | 0.005291005 | 0.0003779289 | 0.04157218 | 0.08956916 | 0.1659108 | 0.01247166 | 0.005291005 | 0.04006047 | 0.2033258 | 0.0143613 | 0.008692366 | 0.02191988 | 0.006424792 | 0.03439153 | 0.08730159 | 0.007936508 | 0.05177627 | 0.003779289 | 0.01058201 | 0.2641723 | 0.03061224 | 0.004913076 | 0.009826153 | 0.003401361 | 0.006802721 | 0.004913076 | 0.01814059 | 0.02267574 | 0.0718065 | 0.00718065 | 0.01662887 | 0.004535147 | 0.2736206 | 0.4512472 | 0.01587302 | 0 | 0.09674981 | 0.1103553 | 0.06462585 | 0.03817082 | 0.002645503 | 0.05668934 | 0.03779289 | 0.006424792 | 0.01549509 | 0.009070295 | 0.04799698 | 0.3034769 | 0.03401361 | 0.5506425 | 0.03514739 | 0 | 0.2936508 | 0.01662887 | 0.0170068 | 0.04686319 | 0.1084656 | 0.001889645 | 0.08654573 | 0.3272865 | 0.0260771 | 0.02834467 | 0.09334845 | 0.0003779289 | 0.02721088 | 0.01587302 | 0.03703704 | 0.01511716 | 0.002645503 | 0.0003779289 | 0.1734694 | 0.02040816 | 0.001889645 | 0.0007558579 | 0.0600907 | 0.01284958 | 0.3261527 | 0.1069539 | 0.001889645 | 0.1587302 | 0.05253212 | 0.1991686 | 0.02229781 | 0.02229781 | 0.03514739 | 0.01473923 | 0.0287226 | 0.02910053 | 0 | 0.01776266 | 0.007936508 | 0.006424792 | 0 | 0.1829176 | 0.02343159 | 0.02078609 | 0.0003779289 | 0.009826153 | 0.002267574 | 0.004157218 | 0.08843537 | 0.09637188 | 0.004535147 | 0.05291005 | 0.3877551 | 0.3571429 | 0.2849584 | 0.08956916 | 0.01814059 | 0.003401361 | 0.0143613 | 0.04346183 | 0.1855631 | 0.03703704 | 0.004157218 | 0.01095994 | 0.003023432 | 0.008692366 | 0.03476946 | 0.4591837 | 0.04195011 | 0.008314437 | 0.02834467 | 0.02834467 | 0.1130008 | 0.111867 | 0.02456538 | 0.009070295 | 0.1617536 | 0.02305367 | 0.0313681 | 0.01284958 | 0.005668934 | 0.02305367 | 0.006802721 | 0.04195011 | 0.01889645 | 0.01473923 | 0 | 0.6213152 | 0.03628118 | 0.0117158 | 0.006802721 | 0.07974301 | 0.007936508 | 0.03174603 | 0.0007558579 | 0.001511716 | 0.1216931 | 0.002645503 | 0.0117158 | 0.0287226 | 0.008314437 | 0.009070295 | 0.01965231 | 0.01814059 | 0.02116402 | 0.05404384 | 0.01209373 | 0.006802721 | 0.0457294 | 0.008314437 | 0.02116402 | 0.002645503 | 0.001511716 | 0.0143613 | 0.07671958 | 0.07142857 | 0.001133787 | 0.01133787 | 0.03514739 | 0.02305367 | 0.007936508 | 0.04610733 | 0.4368859 | 0.01209373 | 0.009448224 | 0.02267574 | 0 | 0 | 0.01587302 | 0.2018141 | 0.01247166 | 0.001889645 | 0.1613757 | 0.0003779289 | 0.0003779289 | 0.008314437 | 0.0007558579 | 0.2256236 | 0.02343159 | 0.1523054 | 0.06386999 | 0.1235828 | 0.0170068 | 0.249811 | 0.004535147 | 0.009448224 | 0.09372638 | 0.313681 | 0.07482993 | 0.2505669 | 0.007558579 | 0.0287226 | 0.03552532 | 0.008692366 | 0.1311413 | 0.03817082 | 0.03401361 |
7 | greek | 0.08510638 | 0.02723404 | 0.02468085 | 0.01276596 | 0.004255319 | 0.005106383 | 0.02893617 | 0.002553191 | 0.0587234 | 0.02212766 | 0.06468085 | 0.03234043 | 0.05702128 | 0 | 0.04680851 | 0.1029787 | 0.3387234 | 0.006808511 | 0.0587234 | 0.1055319 | 0.08595745 | 0.002553191 | 0.05957447 | 0.01191489 | 0.1421277 | 0.002553191 | 0.001702128 | 0.01021277 | 0.02553191 | 0.001702128 | 0.02723404 | 0.02468085 | 0.02723404 | 0.005957447 | 0.4919149 | 0.04680851 | 0.1948936 | 0.02723404 | 0.004255319 | 0.01191489 | 0 | 0.007659574 | 0 | 0.01191489 | 0.006808511 | 0.2314894 | 0.005106383 | 0.008510638 | 0.106383 | 0.2748936 | 0.02042553 | 0.002553191 | 0.004255319 | 0.0008510638 | 0.007659574 | 0.09021277 | 0.01361702 | 0.009361702 | 0 | 0.0187234 | 0.04595745 | 0.03404255 | 0.2170213 | 0.03829787 | 0.2246809 | 0.04170213 | 0.002553191 | 0.001702128 | 0.05957447 | 0.01446809 | 0.1480851 | 0.04680851 | 0.0212766 | 0.44 | 0.1753191 | 0.05446809 | 0 | 0.1948936 | 0.02723404 | 0.03914894 | 0.01787234 | 0.3812766 | 0.01787234 | 0.02723404 | 0.0008510638 | 0 | 0.03744681 | 0.05617021 | 0.1157447 | 0.02978723 | 0.7582979 | 0.03659574 | 0 | 0.5361702 | 0.005957447 | 0.005957447 | 0.01446809 | 0.09617021 | 0.1838298 | 0.1395745 | 0.5531915 | 0.02978723 | 0.001702128 | 0.005957447 | 0 | 0.0612766 | 0.01361702 | 0 | 0.01361702 | 0.001702128 | 0.001702128 | 0.3795745 | 0.1310638 | 0.0008510638 | 0.0008510638 | 0.07489362 | 0.1157447 | 0.1268085 | 0.07744681 | 0.01702128 | 0.1208511 | 0.006808511 | 0.5480851 | 0.01021277 | 0.06978723 | 0.01191489 | 0.005106383 | 0.02042553 | 0.03234043 | 0 | 0.01361702 | 0.005957447 | 0.01021277 | 0 | 0.07574468 | 0.06978723 | 0.1429787 | 0 | 0.01106383 | 0.0008510638 | 0.01106383 | 0.02212766 | 0.02297872 | 0.002553191 | 0.04085106 | 0.7021277 | 0.7795745 | 0.4791489 | 0.03914894 | 0.3429787 | 0 | 0.02723404 | 0.04170213 | 0.2195745 | 0.03319149 | 0.03489362 | 0.01021277 | 0.0008510638 | 0.006808511 | 0.02382979 | 0.7361702 | 0.005106383 | 0.1285106 | 0.04425532 | 0.01787234 | 0.08 | 0.08425532 | 0.1582979 | 0.01021277 | 0.2561702 | 0.005106383 | 0.01021277 | 0.05446809 | 0.009361702 | 0.02468085 | 0.001702128 | 0.05361702 | 0.001702128 | 0.002553191 | 0 | 0.6357447 | 0.05276596 | 0.002553191 | 0.01787234 | 0.03319149 | 0.05531915 | 0.02978723 | 0.0008510638 | 0.01021277 | 0.02468085 | 0.001702128 | 0.005106383 | 0.005106383 | 0 | 0.01361702 | 0.01191489 | 0.03404255 | 0.05617021 | 0.0493617 | 0.006808511 | 0.01446809 | 0.02042553 | 0.003404255 | 0.02042553 | 0.005106383 | 0.005106383 | 0.09361702 | 0.07914894 | 0.02297872 | 0.003404255 | 0.005957447 | 0.008510638 | 0.01702128 | 0.02723404 | 0.02042553 | 0.1242553 | 0.01361702 | 0.001702128 | 0.003404255 | 0 | 0 | 0.009361702 | 0.06042553 | 0.005106383 | 0 | 0.4221277 | 0.004255319 | 0 | 0.01191489 | 0.004255319 | 0.05276596 | 0.0008510638 | 0.02978723 | 0.04680851 | 0.146383 | 0.01361702 | 0.1574468 | 0.01617021 | 0.02723404 | 0.002553191 | 0.1489362 | 0.04765957 | 0.1693617 | 0.002553191 | 0.01276596 | 0.03829787 | 0.2382979 | 0.02382979 | 0.05957447 | 0.04170213 |
8 | indian | 0.04995005 | 0.007992008 | 0.04129204 | 0.002664003 | 0.01498501 | 0.001665002 | 0.02364302 | 0 | 0.03663004 | 0.001998002 | 0.004995005 | 0.07692308 | 0.03962704 | 0.0003330003 | 0.01931402 | 0.04995005 | 0.2291042 | 0.01431901 | 0.08424908 | 0.02664003 | 0.08558109 | 0.006327006 | 0.06393606 | 0.07326007 | 0.1428571 | 0.006993007 | 0.01232101 | 0.05361305 | 0 | 0.1958042 | 0.07092907 | 0.1245421 | 0.01132201 | 0.001332001 | 0.02064602 | 0.004329004 | 0.2710623 | 0.06560107 | 0.1252081 | 0.5141525 | 0.0006660007 | 0.000999001 | 0.0006660007 | 0.002331002 | 0.001665002 | 0.2540793 | 0.009324009 | 0.3476523 | 0.1814852 | 0.3010323 | 0.01631702 | 0.2104562 | 0.006327006 | 0.005661006 | 0.001665002 | 0.06959707 | 0.3846154 | 0.02997003 | 0.0003330003 | 0.007326007 | 0.0952381 | 0.008991009 | 0.000999001 | 0.04562105 | 0.03230103 | 0.5204795 | 0.2870463 | 0.004662005 | 0.05228105 | 0.002331002 | 0.001998002 | 0.005328005 | 0.000999001 | 0.07592408 | 0.04428904 | 0.02064602 | 0 | 0.01764902 | 0.006327006 | 0.03829504 | 0.05028305 | 0.001665002 | 0.02297702 | 0.02630703 | 0.01165501 | 0.008991009 | 0.04562105 | 0.002664003 | 0.1768232 | 0.02331002 | 0.5544456 | 0.05661006 | 0.2877123 | 0.5444555 | 0.4911755 | 0.01731602 | 0.006327006 | 0.03163503 | 0.04695305 | 0.3100233 | 0.9297369 | 0.01798202 | 0 | 0.03596404 | 0 | 0.02097902 | 0.02630703 | 0.00965701 | 0.001665002 | 0.0003330003 | 0.04229104 | 0.1994672 | 0 | 0.003330003 | 0.007992008 | 0.08558109 | 0.03696304 | 0.03962704 | 0.04528805 | 0.000999001 | 0.3203463 | 0.001998002 | 0.1958042 | 0.01032301 | 0.003996004 | 0.02630703 | 0.09057609 | 0.01665002 | 0.02930403 | 0.3323343 | 0.004329004 | 0.01132201 | 0.007992008 | 0 | 0.2117882 | 0.05128205 | 0.07425907 | 0 | 0.007992008 | 0.0003330003 | 0.001332001 | 0.01431901 | 0.1751582 | 0.002664003 | 0.02930403 | 0.6763237 | 0.1411921 | 0.6140526 | 0.007659008 | 0.000999001 | 0.0006660007 | 0.08091908 | 0.001332001 | 0.01565102 | 0.2061272 | 0.000999001 | 0.0959041 | 0.03296703 | 0.0006660007 | 0.06227106 | 0.5487845 | 0.05394605 | 0.1378621 | 0.02464202 | 0.008658009 | 0.1598402 | 0.5071595 | 0.05661006 | 0.03896104 | 0.2947053 | 0.006660007 | 0.004662005 | 0.1974692 | 0.001665002 | 0.01798202 | 0.04961705 | 0.0003330003 | 0.03829504 | 0 | 0.0006660007 | 0.7998668 | 0.04695305 | 0.0003330003 | 0.01198801 | 0.03862804 | 0.005328005 | 0.5790876 | 0.04528805 | 0.01665002 | 0.01864802 | 0 | 0.000999001 | 0.001332001 | 0 | 0.00999001 | 0.00965701 | 0.02497502 | 0.07492507 | 0.01931402 | 0.01132201 | 0.01565102 | 0.02197802 | 0.000999001 | 0.009324009 | 0.01165501 | 0 | 0.06093906 | 0.02630703 | 0.01265401 | 0.004662005 | 0.00965701 | 0.01465201 | 0.005661006 | 0.08658009 | 0.03529804 | 0.1774892 | 0.03163503 | 0.007659008 | 0.002331002 | 0 | 0.006660007 | 0.04395604 | 0.006660007 | 0.005661006 | 0.01365301 | 0.4192474 | 0.002664003 | 0.1694972 | 0.003663004 | 0.2437562 | 0.02963703 | 0.01498501 | 0.005994006 | 0.2477522 | 0.05094905 | 0.01998002 | 0.3220113 | 0.01631702 | 0.02830503 | 0.007659008 | 0.08225108 | 0.06227106 | 0.01831502 | 0.0006660007 | 0.02430902 | 0.07359307 | 0.1918082 | 0.000999001 | 0.007326007 | 0.01431901 |
9 | irish | 0.3283358 | 0.02998501 | 0.01949025 | 0.02248876 | 0.04647676 | 0.002998501 | 0.01649175 | 0.09445277 | 0.3823088 | 0.008995502 | 0.004497751 | 0.07646177 | 0.0149925 | 0 | 0.17991 | 0.01649175 | 0.2128936 | 0.02098951 | 0.005997001 | 0.05547226 | 0.007496252 | 0 | 0.09145427 | 0.1124438 | 0.5007496 | 0.143928 | 0.131934 | 0.005997001 | 0.005997001 | 0.007496252 | 0.1814093 | 0.01649175 | 0.06296852 | 0.05847076 | 0.1244378 | 0.017991 | 0.09895052 | 0 | 0.002998501 | 0.004497751 | 0 | 0.01649175 | 0.00149925 | 0.02998501 | 0.05547226 | 0.1289355 | 0.02398801 | 0.007496252 | 0.06446777 | 0.08845577 | 0.01349325 | 0.005997001 | 0.01349325 | 0.01049475 | 0.02398801 | 0.1034483 | 0.002998501 | 0.06446777 | 0 | 0.008995502 | 0.2608696 | 0.01649175 | 0.002998501 | 0.01049475 | 0.007496252 | 0.004497751 | 0 | 0.02848576 | 0.01649175 | 0.02098951 | 0.01049475 | 0.008995502 | 0.004497751 | 0.1649175 | 0.3223388 | 0 | 0 | 0.017991 | 0.06446777 | 0.05247376 | 0.01049475 | 0 | 0.011994 | 0.02248876 | 0.008995502 | 0.00149925 | 0.005997001 | 0.01349325 | 0.5112444 | 0.03448276 | 0.2713643 | 0.03298351 | 0 | 0.1604198 | 0.03898051 | 0.02098951 | 0.06146927 | 0.06596702 | 0.002998501 | 0.1064468 | 0.3328336 | 0.007496252 | 0.02248876 | 0.06896552 | 0 | 0.017991 | 0.011994 | 0.0149925 | 0.007496252 | 0 | 0.004497751 | 0.07496252 | 0 | 0 | 0.002998501 | 0.04947526 | 0.06146927 | 0.1604198 | 0.04647676 | 0.004497751 | 0.08095952 | 0.06146927 | 0.09145427 | 0.011994 | 0.004497751 | 0.03148426 | 0.01049475 | 0.0149925 | 0.06596702 | 0 | 0.011994 | 0.02548726 | 0 | 0 | 0.2788606 | 0.011994 | 0.0149925 | 0 | 0.02998501 | 0 | 0.004497751 | 0.03898051 | 0.06596702 | 0 | 0.05847076 | 0.2068966 | 0.09745127 | 0.3568216 | 0.06746627 | 0.005997001 | 0.004497751 | 0.02398801 | 0.01049475 | 0.1244378 | 0.03448276 | 0.00149925 | 0.03448276 | 0.007496252 | 0.01049475 | 0.02398801 | 0.4257871 | 0.02098951 | 0.01649175 | 0.004497751 | 0.03748126 | 0.4107946 | 0.2473763 | 0.004497751 | 0.07496252 | 0.1064468 | 0.0149925 | 0.01949025 | 0.002998501 | 0 | 0.01649175 | 0.002998501 | 0.02848576 | 0 | 0.01649175 | 0.00149925 | 0.6926537 | 0.06296852 | 0.04647676 | 0.02098951 | 0.02548726 | 0.01349325 | 0.06296852 | 0 | 0.005997001 | 0.01649175 | 0.017991 | 0.002998501 | 0.011994 | 0.002998501 | 0.01949025 | 0.03448276 | 0 | 0.004497751 | 0.02398801 | 0.017991 | 0.1829085 | 0.02698651 | 0.00149925 | 0.04797601 | 0.01049475 | 0 | 0.01049475 | 0.09295352 | 0.01949025 | 0.01049475 | 0.00149925 | 0.01949025 | 0.01049475 | 0.008995502 | 0.07346327 | 0.4587706 | 0.017991 | 0.011994 | 0.01649175 | 0 | 0 | 0 | 0.1034483 | 0.007496252 | 0 | 0.06596702 | 0.00149925 | 0.00149925 | 0.005997001 | 0 | 0.1304348 | 0.0149925 | 0.07646177 | 0.1064468 | 0.06146927 | 0.01049475 | 0.1814093 | 0.005997001 | 0.08245877 | 0.05397301 | 0.1514243 | 0.1034483 | 0.04497751 | 0.03448276 | 0.008995502 | 0.04047976 | 0.017991 | 0.02698651 | 0.02998501 | 0.005997001 |
10 | italian | 0.1172493 | 0.001531003 | 0.03393723 | 0.02143404 | 0.004337841 | 0.003189589 | 0.03023731 | 0.0294718 | 0.03891299 | 0.04746109 | 0.2558051 | 0.0349579 | 0.06149528 | 0.0001275836 | 0.07438122 | 0.09249809 | 0.3155142 | 0.01046185 | 0.05179893 | 0.1122735 | 0.07017096 | 0.02449604 | 0.1287318 | 0.01773412 | 0.2117887 | 0.006124011 | 0.006124011 | 0.005741261 | 0.03967849 | 0.001148252 | 0.05537127 | 0.008292932 | 0.05460577 | 0.01479969 | 0.7454708 | 0.02857872 | 0.2341158 | 0.005230926 | 0.005230926 | 0.01365144 | 0.0003827507 | 0.00357234 | 0.0007655014 | 0.0163307 | 0.02679255 | 0.2010717 | 0.003189589 | 0.006634345 | 0.01607553 | 0.2507017 | 0.01990304 | 0.002934422 | 0.008165348 | 0.006506762 | 0.01212044 | 0.1044909 | 0.002551671 | 0.02781322 | 0.009951518 | 0.01492728 | 0.1268181 | 0.05090584 | 0.01696861 | 0.1120184 | 0.005103343 | 0.004720592 | 0.001148252 | 0.004720592 | 0.06545037 | 0.01365144 | 0.003062006 | 0.02845114 | 0.01658586 | 0.3716509 | 0.2287573 | 0.02908905 | 0 | 0.1737688 | 0.04146466 | 0.04848176 | 0.03227864 | 0.01377903 | 0.02755805 | 0.03368206 | 0.002041337 | 0.003955091 | 0.06825721 | 0.07616739 | 0.1736412 | 0.03151314 | 0.6368972 | 0.04325083 | 0.0001275836 | 0.5324062 | 0.00535851 | 0.00893085 | 0.01454453 | 0.2649911 | 0.001148252 | 0.09645318 | 0.3803266 | 0.03023731 | 0.01237561 | 0.048099 | 0 | 0.01671345 | 0.03176831 | 0.00714468 | 0.1437867 | 0.004848176 | 0.002551671 | 0.1237561 | 0.01990304 | 0.003955091 | 0.00178617 | 0.08497066 | 0.004593008 | 0.1647104 | 0.09300842 | 0.02079612 | 0.1560347 | 0.01352386 | 0.1787446 | 0.02258229 | 0.009441184 | 0.01046185 | 0.006379178 | 0.04159224 | 0.02411329 | 0.0001275836 | 0.008037765 | 0.009823935 | 0.01173769 | 0.0002551671 | 0.09402909 | 0.04950242 | 0.02156162 | 0 | 0.01237561 | 0.002806838 | 0.1593519 | 0.1075529 | 0.01875478 | 0.04006124 | 0.03023731 | 0.6612656 | 0.6362592 | 0.3466446 | 0.03687165 | 0.125925 | 0.001148252 | 0.01135494 | 0.3133452 | 0.2227609 | 0.05741261 | 0.1294973 | 0.0264098 | 0.0006379178 | 0.005486093 | 0.03406481 | 0.7231437 | 0.004848176 | 0.004337841 | 0.04848176 | 0.0294718 | 0.0364889 | 0.09224292 | 0.04478183 | 0.0108446 | 0.2619291 | 0.01569278 | 0.02181679 | 0.04682317 | 0.08484307 | 0.02985455 | 0.0007655014 | 0.06481245 | 0.004720592 | 0.03534065 | 0.001658586 | 0.6247767 | 0.1710896 | 0.07501914 | 0.008037765 | 0.04337841 | 0.07119163 | 0.02283746 | 0.000893085 | 0.002168921 | 0.04031641 | 0.004848176 | 0.0163307 | 0.009951518 | 0.008420515 | 0.003189589 | 0.07540189 | 0.03164072 | 0.0442715 | 0.05600919 | 0.008165348 | 0.009186017 | 0.05294718 | 0.01020669 | 0.01428936 | 0.002934422 | 0.04554733 | 0.07323297 | 0.08254657 | 0.02079612 | 0.0006379178 | 0.0186272 | 0.01097219 | 0.01237561 | 0.003827507 | 0.03444756 | 0.1758102 | 0.02538913 | 0.002041337 | 0.004465425 | 0 | 0.0002551671 | 0.008292932 | 0.06685379 | 0.008037765 | 0.002168921 | 0.4268946 | 0.002168921 | 0.0007655014 | 0.02373054 | 0.0002551671 | 0.07399847 | 0.01186527 | 0.04159224 | 0.06902271 | 0.09874968 | 0.02334779 | 0.1892064 | 0.008037765 | 0.02207196 | 0.03559582 | 0.2181679 | 0.05754019 | 0.1940546 | 0.007655014 | 0.03164072 | 0.04669559 | 0.004337841 | 0.02883389 | 0.03470273 | 0.04337841 |
11 | jamaican | 0.07984791 | 0.3707224 | 0.005703422 | 0.01140684 | 0.01520913 | 0.01140684 | 0.01330798 | 0.01901141 | 0.09315589 | 0.001901141 | 0.01520913 | 0.03992395 | 0.1140684 | 0 | 0.1121673 | 0.1406844 | 0.4220532 | 0.01140684 | 0.06653992 | 0.06653992 | 0.07794677 | 0.001901141 | 0.05513308 | 0.2072243 | 0.1749049 | 0.003802281 | 0.01901141 | 0.01901141 | 0.001901141 | 0.003802281 | 0.09315589 | 0.09505703 | 0.03041825 | 0.003802281 | 0.03231939 | 0.007604563 | 0.338403 | 0 | 0.1121673 | 0.108365 | 0.009505703 | 0.007604563 | 0.005703422 | 0.01330798 | 0.007604563 | 0.1330798 | 0.03231939 | 0.06463878 | 0.2414449 | 0.2756654 | 0.01140684 | 0.2661597 | 0.0608365 | 0.01901141 | 0.001901141 | 0.08745247 | 0.03802281 | 0.04562738 | 0.02471483 | 0.009505703 | 0.04562738 | 0.05893536 | 0 | 0.03041825 | 0.001901141 | 0.05703422 | 0.1444867 | 0.08365019 | 0.03041825 | 0.01140684 | 0.003802281 | 0.01330798 | 0.001901141 | 0.3098859 | 0.1235741 | 0.001901141 | 0 | 0.01140684 | 0.04372624 | 0.02091255 | 0 | 0 | 0.02091255 | 0.02091255 | 0 | 0.02471483 | 0.06463878 | 0.005703422 | 0.2110266 | 0.009505703 | 0.4296578 | 0.01140684 | 0.003802281 | 0.595057 | 0.2357414 | 0.001901141 | 0.02281369 | 0.0608365 | 0.001901141 | 0.2072243 | 1.030418 | 0.02471483 | 0.009505703 | 0.003802281 | 0 | 0.01520913 | 0.1197719 | 0.01901141 | 0.001901141 | 0 | 0.05703422 | 0.2585551 | 0 | 0.001901141 | 0.06273764 | 0.06273764 | 0.001901141 | 0.04562738 | 0.01711027 | 0.01711027 | 0.1121673 | 0.001901141 | 0.04372624 | 0.001901141 | 0.01140684 | 0.04942966 | 0.2908745 | 0.03231939 | 0.003802281 | 0.005703422 | 0.01711027 | 0.03231939 | 0.007604563 | 0 | 0.2775665 | 0.05893536 | 0.003802281 | 0 | 0.01711027 | 0 | 0.007604563 | 0.009505703 | 0.03231939 | 0.005703422 | 0.2281369 | 0.513308 | 0.1387833 | 0.7110266 | 0.08935361 | 0.01330798 | 0.001901141 | 0.05323194 | 0.003802281 | 0.02851711 | 0.03041825 | 0.001901141 | 0.03612167 | 0.01520913 | 0.01520913 | 0.02661597 | 1.171103 | 0.01901141 | 0.02091255 | 0.009505703 | 0.0608365 | 0.1007605 | 0.3840304 | 0.04942966 | 0.02091255 | 0.2338403 | 0.003802281 | 0.005703422 | 0.1273764 | 0 | 0.01711027 | 0.03041825 | 0.01140684 | 0 | 0.01520913 | 0.003802281 | 0.7965779 | 0.3288973 | 0 | 0.2148289 | 0.03992395 | 0.09885932 | 0.03612167 | 0.009505703 | 0.003802281 | 0.01711027 | 0 | 0.001901141 | 0.007604563 | 0 | 0.001901141 | 0.01330798 | 0.03802281 | 0.0608365 | 0.02851711 | 0.01140684 | 0.02661597 | 0.03231939 | 0.003802281 | 0.01711027 | 0.1539924 | 0 | 0.02661597 | 0.03041825 | 0.02091255 | 0.01520913 | 0.01140684 | 0.02091255 | 0.01711027 | 0.01330798 | 0.07224335 | 0.3878327 | 0.0513308 | 0.02281369 | 0.01520913 | 0 | 0 | 0.03231939 | 0.5627376 | 0.005703422 | 0 | 0.161597 | 0.009505703 | 0.02851711 | 0.005703422 | 0.02471483 | 0.03612167 | 0.03041825 | 0.07034221 | 0.230038 | 0.1711027 | 0.009505703 | 0.3992395 | 0.009505703 | 0.003802281 | 0.005703422 | 0.1730038 | 0.01901141 | 0.04562738 | 0.01901141 | 0.02281369 | 0.06653992 | 0.003802281 | 0.003802281 | 0.01330798 | 0.009505703 |
12 | japanese | 0.04848911 | 0.0007027407 | 0.009135629 | 0.003513703 | 0.01264933 | 0.04216444 | 0.02670415 | 0.005621926 | 0.03865074 | 0.002810963 | 0.007027407 | 0.01686578 | 0.03583978 | 0.0154603 | 0.04989459 | 0.03583978 | 0.1082221 | 0.01335207 | 0.0639494 | 0.00983837 | 0.04567814 | 0.01194659 | 0.04708363 | 0.08081518 | 0.07378777 | 0 | 0.07378777 | 0.04427266 | 0 | 0.02670415 | 0.1433591 | 0.01827126 | 0.01335207 | 0.0007027407 | 0.02459592 | 0.006324666 | 0.1939564 | 0.002108222 | 0.01967674 | 0.1278988 | 0.01194659 | 0.0007027407 | 0.0007027407 | 0.02600141 | 0.005621926 | 0.05200281 | 0.005621926 | 0.06535488 | 0.01616304 | 0.08151792 | 0.006324666 | 0.03021785 | 0.0224877 | 0.005621926 | 0.002810963 | 0.05059733 | 0.04567814 | 0.08011244 | 0 | 0.004919185 | 0.04286718 | 0.002810963 | 0 | 0.0154603 | 0.08151792 | 0.04567814 | 0.02529866 | 0.037948 | 0.004216444 | 0.009135629 | 0.003513703 | 0.004216444 | 0.004919185 | 0.08995081 | 0.2347154 | 0.02740689 | 0 | 0.0154603 | 0.01616304 | 0.008432888 | 0.008432888 | 0 | 0.07238229 | 0.01475755 | 0.04778637 | 0.018974 | 0.08432888 | 0.004216444 | 0.1426564 | 0.01054111 | 0.2839072 | 0.03092059 | 0.03232607 | 0.2515812 | 0.3239635 | 0.002108222 | 0.03373155 | 0.01335207 | 0.0007027407 | 0.2319044 | 0.1834153 | 0.005621926 | 0.002810963 | 0.01054111 | 0.006324666 | 0.05551651 | 0.02810963 | 0.00983837 | 0.001405481 | 0.0007027407 | 0.003513703 | 0.1004919 | 0 | 0.003513703 | 0.02600141 | 0.056922 | 0 | 0.08713985 | 0.01264933 | 0.001405481 | 0.07800422 | 0.01335207 | 0.0983837 | 0.002108222 | 0.007730148 | 0.03373155 | 0.0449754 | 0.05551651 | 0.0007027407 | 0.03583978 | 0.03583978 | 0.01967674 | 0.01335207 | 0 | 0.07167955 | 0.03443429 | 0.003513703 | 0.2825018 | 0.01054111 | 0 | 0.002810963 | 0.1033029 | 0.03373155 | 0.09557273 | 0.002108222 | 0.6331694 | 0.056922 | 0.3316936 | 0.02529866 | 0 | 0.009135629 | 0.006324666 | 0.005621926 | 0.007730148 | 0.1293043 | 0.002108222 | 0.05270555 | 0.0224877 | 0.002810963 | 0.04075896 | 0.2740689 | 0.007027407 | 0.01124385 | 0.005621926 | 0.07870696 | 0.06746311 | 0.1658468 | 0.00983837 | 0.002810963 | 0.1257906 | 0.02389318 | 0.009135629 | 0.3893183 | 0.0007027407 | 0.01194659 | 0.03583978 | 0 | 0.004919185 | 0 | 0 | 0.4082923 | 0.6605762 | 0.001405481 | 0.1658468 | 0.05270555 | 0.01686578 | 0.2431483 | 0.001405481 | 0.3654252 | 0.02108222 | 0.0007027407 | 0.005621926 | 0.01054111 | 0.09697822 | 0.006324666 | 0.01967674 | 0.04989459 | 0.04567814 | 0.02881237 | 0.0154603 | 0.01405481 | 0.08011244 | 0.008432888 | 0.002108222 | 0.547435 | 0.001405481 | 0.05130007 | 0.018974 | 0.002810963 | 0.02951511 | 0.007027407 | 0.07800422 | 0.03513703 | 0.007027407 | 0.0449754 | 0.4567814 | 0.02881237 | 0.007027407 | 0.007027407 | 0 | 0.004919185 | 0.04567814 | 0.002108222 | 0.09627547 | 0.08924807 | 0.05481377 | 0.0007027407 | 0.02037948 | 0.002108222 | 0.02389318 | 0.01475755 | 0 | 0.01405481 | 0.2052003 | 0.2368236 | 0.007730148 | 0.3366128 | 0.01405481 | 0.01124385 | 0.001405481 | 0.2108222 | 0.01756852 | 0.04567814 | 0.0154603 | 0.004919185 | 0.03373155 | 0.002810963 | 0.03443429 | 0.00983837 | 0.01264933 |
13 | korean | 0.0373494 | 0 | 0.001204819 | 0 | 0.03253012 | 0.002409639 | 0.02650602 | 0.009638554 | 0.01204819 | 0.004819277 | 0.002409639 | 0.003614458 | 0.07710843 | 0.03855422 | 0.2 | 0.0373494 | 0.2313253 | 0.004819277 | 0.0313253 | 0.003614458 | 0.01204819 | 0.004819277 | 0.04457831 | 0.2024096 | 0.01807229 | 0.002409639 | 0.1325301 | 0.05542169 | 0 | 0 | 0.2168675 | 0.01927711 | 0.006024096 | 0.007228916 | 0.01566265 | 0 | 0.1313253 | 0.001204819 | 0.09277108 | 0.1951807 | 0.02409639 | 0.002409639 | 0 | 0.0253012 | 0 | 0.05421687 | 0.01445783 | 0.02048193 | 0.007228916 | 0.2180723 | 0.01927711 | 0.009638554 | 0.02409639 | 0 | 0 | 0.08795181 | 0.007228916 | 0.07951807 | 0 | 0.003614458 | 0.007228916 | 0.001204819 | 0 | 0.05662651 | 0.09759036 | 0.001204819 | 0.002409639 | 0.08313253 | 0.001204819 | 0.003614458 | 0 | 0.001204819 | 0.003614458 | 0.05421687 | 0.2048193 | 0.004819277 | 0 | 0.004819277 | 0.007228916 | 0.004819277 | 0 | 0 | 0.009638554 | 0.002409639 | 0.04216867 | 0.07108434 | 0.1795181 | 0.004819277 | 0.1060241 | 0.004819277 | 0.2240964 | 0.003614458 | 0 | 0.7698795 | 0.3855422 | 0.004819277 | 0.02891566 | 0.001204819 | 0.001204819 | 0.4192771 | 0.2301205 | 0.001204819 | 0.003614458 | 0.003614458 | 0.004819277 | 0.09638554 | 0.04819277 | 0.008433735 | 0 | 0.001204819 | 0.01927711 | 0.07590361 | 0 | 0.001204819 | 0.01566265 | 0.08915663 | 0.007228916 | 0.0626506 | 0.01204819 | 0.01325301 | 0.04698795 | 0.01084337 | 0.02409639 | 0.002409639 | 0.05542169 | 0.04337349 | 0.01325301 | 0.06024096 | 0 | 0 | 0.01084337 | 0.02048193 | 0.01204819 | 0 | 0.007228916 | 0.1349398 | 0 | 0.07108434 | 0.008433735 | 0 | 0.001204819 | 0.1072289 | 0.01566265 | 0.06746988 | 0 | 0.9819277 | 0.05060241 | 0.753012 | 0.009638554 | 0 | 0.02409639 | 0.01325301 | 0 | 0.003614458 | 0.1156627 | 0.002409639 | 0.01084337 | 0.02289157 | 0 | 0.0253012 | 0.6963855 | 0.002409639 | 0.004819277 | 0.002409639 | 0.1084337 | 0.0626506 | 0.07710843 | 0.009638554 | 0.002409639 | 0.2855422 | 0.02650602 | 0.1096386 | 0.4987952 | 0 | 0.02891566 | 0.01927711 | 0.001204819 | 0 | 0 | 0.001204819 | 0.460241 | 0.8144578 | 0.004819277 | 0.2373494 | 0.04337349 | 0.01204819 | 0.4433735 | 0.004819277 | 1.080723 | 0.008433735 | 0.001204819 | 0.001204819 | 0.01445783 | 0.09277108 | 0.01084337 | 0.02409639 | 0.0373494 | 0.01325301 | 0.02891566 | 0.003614458 | 0.01084337 | 0.1012048 | 0 | 0.003614458 | 0.6554217 | 0.001204819 | 0.07831325 | 0.01807229 | 0 | 0.05542169 | 0.004819277 | 0.05662651 | 0.1024096 | 0.004819277 | 0.03975904 | 0.5879518 | 0.05301205 | 0 | 0.05542169 | 0 | 0.006024096 | 0.01566265 | 0.001204819 | 0.2301205 | 0.08072289 | 0.008433735 | 0.01204819 | 0 | 0.006024096 | 0 | 0.004819277 | 0 | 0.004819277 | 0.1626506 | 0.260241 | 0.004819277 | 0.3349398 | 0 | 0.006024096 | 0 | 0.1783133 | 0.006024096 | 0.1012048 | 0.001204819 | 0.002409639 | 0.04819277 | 0.001204819 | 0.01204819 | 0.003614458 | 0.06506024 |
14 | mexican | 0.05125815 | 0.01025163 | 0.0128922 | 0.01335819 | 0.01429015 | 0.1713265 | 0.006679093 | 0.0195713 | 0.0397639 | 0.003727866 | 0.009785648 | 0.02920162 | 0.2656104 | 0 | 0.13032 | 0.1441441 | 0.3487108 | 0.00621311 | 0.09443927 | 0.01693072 | 0.1324946 | 0.002485244 | 0.1332712 | 0.0408512 | 0.08962411 | 0.004659832 | 0.02982293 | 0.04147251 | 0.004193849 | 0.0003106555 | 0.02625039 | 0.06601429 | 0.01724138 | 0.176297 | 0.4622554 | 0.01413482 | 0.3841255 | 0.002485244 | 0.2314383 | 0.472041 | 0 | 0.06896552 | 0.07844051 | 0.003727866 | 0.02003728 | 0.3226157 | 0.02236719 | 0.4339857 | 0.04954955 | 0.1814228 | 0.02625039 | 0.008387698 | 0.005747126 | 0.02143523 | 0.003106555 | 0.1116806 | 0.03386145 | 0.3342653 | 0.006523765 | 0.007611059 | 0.296676 | 0.007145076 | 0.006057782 | 0.0240758 | 0.01056229 | 0.3201305 | 0.0009319664 | 0.007300404 | 0.1251942 | 0.002951227 | 0.002485244 | 0.007455732 | 0.01926064 | 0.1296987 | 0.1015843 | 0.000621311 | 0.0621311 | 0.03199751 | 0.01615409 | 0.0453557 | 0.0009319664 | 0.006523765 | 0.01786269 | 0.02236719 | 0.002795899 | 0.003727866 | 0.02081392 | 0.004038521 | 0.221342 | 0.01879466 | 0.4168997 | 0.05529668 | 0 | 0.500466 | 0.004970488 | 0.003106555 | 0.01196024 | 0.02547375 | 0.01025163 | 0.3022678 | 0.5428705 | 0.02795899 | 0.004193849 | 0.01102827 | 0.0001553277 | 0.01863933 | 0.0537434 | 0.01273687 | 0.006368437 | 0.1326499 | 0.1825101 | 0.2528736 | 0.000621311 | 0.06523765 | 0.005747126 | 0.09583722 | 0.001708605 | 0.06197577 | 0.01755203 | 0.03292948 | 0.08993476 | 0.0004659832 | 0.03681267 | 0.01164958 | 0.07906182 | 0.01832867 | 0.3550792 | 0.03028891 | 0.01724138 | 0 | 0.02578441 | 0.01926064 | 0.009009009 | 0.07766387 | 0.06974216 | 0.0425598 | 0.007766387 | 0.0001553277 | 0.05327742 | 0.0823237 | 0.01475614 | 0.02345449 | 0.007766387 | 0.003727866 | 0.002329916 | 0.462566 | 0.286735 | 0.6890339 | 0.04395775 | 0.1407269 | 0.000621311 | 0.05638397 | 0.007921715 | 0.02283318 | 0.02811432 | 0.01227089 | 0.004970488 | 0.007145076 | 0.004193849 | 0.01025163 | 0.7283318 | 0.008543026 | 0.01102827 | 0.02951227 | 0.06150979 | 0.03572538 | 0.3690587 | 0.09599254 | 0.01133893 | 0.1825101 | 0.02935694 | 0.007455732 | 0.08077043 | 0.002485244 | 0.03836595 | 0.000621311 | 0.002485244 | 0.001087294 | 0.001087294 | 0.1992855 | 0.6088847 | 0.2342342 | 0.01491146 | 0.02671637 | 0.03091022 | 0.1040696 | 0.04411308 | 0.03665735 | 0.009319664 | 0.007145076 | 0.02593973 | 0.01910531 | 0.002795899 | 0.001708605 | 0.018484 | 0.2261572 | 0.02764834 | 0.07564461 | 0.0509475 | 0.02609506 | 0.005902454 | 0.04551103 | 0.03199751 | 0.2054986 | 0.009474992 | 0.002640572 | 0.01693072 | 0.05607331 | 0.01739671 | 0.002329916 | 0.0100963 | 0.009164337 | 0.02640572 | 0.01087294 | 0.03852128 | 0.1071761 | 0.03510407 | 0.007921715 | 0.003261883 | 0.09894377 | 0 | 0.01444548 | 0.01056229 | 0.003727866 | 0.002019261 | 0.44548 | 0.3937558 | 0.0007766387 | 0.02811432 | 0.001553277 | 0.02485244 | 0.01040696 | 0.02329916 | 0.1548618 | 0.06756757 | 0.008698354 | 0.1707052 | 0.0397639 | 0.01770736 | 0.0111836 | 0.1567257 | 0.055452 | 0.02485244 | 0.01040696 | 0.005125815 | 0.06927617 | 0.01786269 | 0.006834421 | 0.01071761 | 0.02283318 |
15 | moroccan | 0.05359318 | 0.05359318 | 0.135201 | 0.007308161 | 0.008526188 | 0.002436054 | 0.01461632 | 0 | 0.01339829 | 0.006090134 | 0.01096224 | 0.02801462 | 0.07064555 | 0 | 0.08282582 | 0.09378806 | 0.3386114 | 0.02192448 | 0.0682095 | 0.02436054 | 0.07917174 | 0.004872107 | 0.2192448 | 0.02801462 | 0.1339829 | 0 | 0.004872107 | 0.02436054 | 0.009744214 | 0.03532278 | 0.2034105 | 0.1729598 | 0.04384896 | 0.002436054 | 0.02923264 | 0.01461632 | 0.4445798 | 0.1607795 | 0.02801462 | 0.05237515 | 0 | 0.002436054 | 0 | 0.002436054 | 0 | 0.408039 | 0.004872107 | 0.3483557 | 0.5030451 | 0.36419 | 0.02801462 | 0.008526188 | 0.001218027 | 0.001218027 | 0.01096224 | 0.05968331 | 0.3069428 | 0.01096224 | 0.00365408 | 0.009744214 | 0.007308161 | 0.008526188 | 0.008526188 | 0.04384896 | 0.009744214 | 0.5505481 | 0.02801462 | 0.001218027 | 0.07917174 | 0.00365408 | 0.007308161 | 0.008526188 | 0.001218027 | 0.2216809 | 0.09013398 | 0.04263094 | 0 | 0.1559074 | 0.00365408 | 0.04872107 | 0.03045067 | 0.01461632 | 0.03288672 | 0.03045067 | 0.001218027 | 0.007308161 | 0.04141291 | 0.08038977 | 0.1096224 | 0.03410475 | 0.8233861 | 0.01096224 | 0.007308161 | 0.5481121 | 0.3495737 | 0.06090134 | 0.01096224 | 0.05602923 | 0.02436054 | 0.1729598 | 1.668697 | 0.05237515 | 0 | 0.001218027 | 0 | 0.1425091 | 0.02557856 | 0.002436054 | 0.007308161 | 0 | 0.002436054 | 0.3154689 | 0.02801462 | 0 | 0.002436054 | 0.1157125 | 0.2070646 | 0.07673569 | 0.09622412 | 0.00365408 | 0.1169306 | 0.004872107 | 0.546894 | 0.03532278 | 0.006090134 | 0.004872107 | 0.02436054 | 0.0499391 | 0.006090134 | 0.007308161 | 0.008526188 | 0.01705238 | 0.002436054 | 0 | 0.02436054 | 0.04141291 | 0.1437272 | 0 | 0.009744214 | 0 | 0.001218027 | 0.007308161 | 0.01096224 | 0.006090134 | 0.03897686 | 0.7771011 | 0.8014616 | 0.6041413 | 0.1084044 | 0.01461632 | 0.001218027 | 0.2959805 | 0.00365408 | 0.2947625 | 0.09744214 | 0.006090134 | 0.0182704 | 0.004872107 | 0 | 0.0499391 | 0.8343484 | 0.009744214 | 0.02070646 | 0.02801462 | 0.007308161 | 0.09500609 | 0.09378806 | 0.0816078 | 0.1181486 | 0.226553 | 0.008526188 | 0.02070646 | 0.02314251 | 0.002436054 | 0.02192448 | 0.009744214 | 0.007308161 | 0.1534714 | 0 | 0.001218027 | 0.7320341 | 0.03045067 | 0.01339829 | 0.006090134 | 0.05115713 | 0.007308161 | 0.1705238 | 0.002436054 | 0.02436054 | 0.0365408 | 0 | 0.006090134 | 0.01218027 | 0 | 0.04019488 | 0.001218027 | 0.009744214 | 0.06211937 | 0.03897686 | 0.03775883 | 0 | 0.07186358 | 0.001218027 | 0.004872107 | 0.004872107 | 0.002436054 | 0.01461632 | 0.05237515 | 0.03532278 | 0.002436054 | 0.05359318 | 0 | 0.01583435 | 0.08769793 | 0.1254568 | 0.1583435 | 0.1266748 | 0 | 0.00365408 | 0 | 0 | 0.06333739 | 0.02679659 | 0.01705238 | 0.001218027 | 0.3751523 | 0 | 0.136419 | 0.009744214 | 0.08891596 | 0.05724726 | 0.001218027 | 0.007308161 | 0.1437272 | 0.0499391 | 0.02436054 | 0.3081608 | 0.02070646 | 0.01461632 | 0.002436054 | 0.08038977 | 0.03410475 | 0.06211937 | 0 | 0.02436054 | 0.08038977 | 0.04141291 | 0.009744214 | 0.03897686 | 0.04872107 |
16 | russian | 0.2515337 | 0.01635992 | 0.05316973 | 0.008179959 | 0.06339468 | 0.00408998 | 0 | 0.02249489 | 0.1513292 | 0.008179959 | 0.01226994 | 0.0797546 | 0.03680982 | 0 | 0.1472393 | 0.03680982 | 0.2167689 | 0.02862986 | 0.01431493 | 0.08384458 | 0.02658487 | 0 | 0.08588957 | 0.03067485 | 0.4355828 | 0.02453988 | 0.1431493 | 0.01840491 | 0.0204499 | 0.01226994 | 0.1635992 | 0.01840491 | 0.08179959 | 0.008179959 | 0.1390593 | 0.01840491 | 0.08793456 | 0.00204499 | 0.00408998 | 0.01840491 | 0 | 0.00204499 | 0 | 0.02862986 | 0.03476483 | 0.1206544 | 0.02862986 | 0.01840491 | 0.06952965 | 0.1411043 | 0.02249489 | 0.00204499 | 0.03885481 | 0.0204499 | 0.02658487 | 0.03067485 | 0.01226994 | 0.03271984 | 0.00204499 | 0.008179959 | 0.3742331 | 0.04907975 | 0 | 0.00204499 | 0.0408998 | 0.01022495 | 0 | 0.0204499 | 0.02249489 | 0.01840491 | 0.2269939 | 0.008179959 | 0.00408998 | 0.1860941 | 0.5725971 | 0.00408998 | 0 | 0.01226994 | 0.08997955 | 0.008179959 | 0.01226994 | 0.00408998 | 0.03476483 | 0.02658487 | 0 | 0.008179959 | 0.01226994 | 0.02249489 | 0.4846626 | 0 | 0.3271984 | 0.0204499 | 0 | 0.1799591 | 0.01840491 | 0.03271984 | 0.03885481 | 0.0204499 | 0.00408998 | 0.1042945 | 0.3251534 | 0.01226994 | 0.02658487 | 0.03680982 | 0 | 0.04498978 | 0.03476483 | 0.008179959 | 0.00408998 | 0 | 0.00408998 | 0.1308793 | 0.00204499 | 0 | 0.01840491 | 0.04703476 | 0.006134969 | 0.2474438 | 0.04703476 | 0.01635992 | 0.0797546 | 0.01840491 | 0.206544 | 0 | 0.00408998 | 0.01635992 | 0 | 0.008179959 | 0.01431493 | 0.00408998 | 0.06339468 | 0.02249489 | 0.00408998 | 0 | 0.200409 | 0.0204499 | 0.008179959 | 0 | 0.01022495 | 0 | 0.008179959 | 0.09611452 | 0.03476483 | 0.01635992 | 0.01635992 | 0.3537832 | 0.1349693 | 0.4274029 | 0.02862986 | 0.00408998 | 0 | 0.0408998 | 0.00408998 | 0.1206544 | 0.03885481 | 0.00204499 | 0.0408998 | 0 | 0 | 0.02862986 | 0.4355828 | 0.02453988 | 0.03067485 | 0.008179959 | 0.05316973 | 0.2617587 | 0.1676892 | 0.01635992 | 0.08179959 | 0.1165644 | 0.01022495 | 0.03271984 | 0.0408998 | 0.006134969 | 0.008179959 | 0.01840491 | 0.006134969 | 0.008179959 | 0.00204499 | 0 | 0.7055215 | 0.06543967 | 0.0204499 | 0.01840491 | 0.02862986 | 0.01431493 | 0.08793456 | 0 | 0 | 0.01840491 | 0.00204499 | 0 | 0.00408998 | 0.00408998 | 0.00408998 | 0.01840491 | 0.008179959 | 0.01431493 | 0.03067485 | 0.03067485 | 0.04907975 | 0.01635992 | 0.01431493 | 0.2597137 | 0.006134969 | 0 | 0.008179959 | 0.01226994 | 0.00408998 | 0 | 0 | 0.02862986 | 0.01840491 | 0.01635992 | 0.0408998 | 0.5132924 | 0.03680982 | 0.006134969 | 0.01226994 | 0 | 0 | 0.00204499 | 0.02249489 | 0.01022495 | 0.00408998 | 0.1676892 | 0.00204499 | 0.00204499 | 0.01226994 | 0 | 0.192229 | 0.01431493 | 0.1288344 | 0.1860941 | 0.1513292 | 0.04498978 | 0.3415133 | 0.00408998 | 0.01635992 | 0.03680982 | 0.194274 | 0.05725971 | 0.07770961 | 0.01226994 | 0.1165644 | 0.01840491 | 0.02658487 | 0.07361963 | 0.0204499 | 0.00408998 |
17 | southern_us | 0.2831019 | 0.01365741 | 0.0162037 | 0.01412037 | 0.0537037 | 0.003009259 | 0.01111111 | 0.09976852 | 0.287037 | 0.007175926 | 0.01898148 | 0.04444444 | 0.03634259 | 0 | 0.01990741 | 0.07013889 | 0.200463 | 0.0150463 | 0.03009259 | 0.05972222 | 0.03356481 | 0.003009259 | 0.09027778 | 0.1451389 | 0.4944444 | 0.1648148 | 0.01481481 | 0.02662037 | 0.001851852 | 0.002083333 | 0.0349537 | 0.07662037 | 0.08101852 | 0.09328704 | 0.2018519 | 0.01111111 | 0.2134259 | 0.000462963 | 0.01574074 | 0.05069444 | 0 | 0.006944444 | 0.005324074 | 0.0212963 | 0.02592593 | 0.1738426 | 0.05277778 | 0.01342593 | 0.09236111 | 0.09166667 | 0.01689815 | 0.02800926 | 0.01550926 | 0.02662037 | 0.0150463 | 0.07199074 | 0.003703704 | 0.1918981 | 0.07361111 | 0.01342593 | 0.2233796 | 0.03888889 | 0.003703704 | 0.03564815 | 0.005787037 | 0.01736111 | 0.006481481 | 0.03912037 | 0.03171296 | 0.02337963 | 0.01018519 | 0.004861111 | 0.0150463 | 0.1159722 | 0.3907407 | 0.0006944444 | 0.0002314815 | 0.02268519 | 0.1368056 | 0.0275463 | 0.003009259 | 0.0009259259 | 0.01689815 | 0.02268519 | 0.01782407 | 0.00162037 | 0.03842593 | 0.01087963 | 0.4275463 | 0.01643519 | 0.2375 | 0.03888889 | 0.000462963 | 0.2284722 | 0.02685185 | 0.009490741 | 0.06689815 | 0.05347222 | 0.003240741 | 0.1826389 | 0.3546296 | 0.02962963 | 0.05162037 | 0.05902778 | 0.000462963 | 0.04074074 | 0.07685185 | 0.03518519 | 0.01203704 | 0.01064815 | 0.02546296 | 0.1375 | 0.0006944444 | 0.02708333 | 0.02361111 | 0.08263889 | 0.0006944444 | 0.2162037 | 0.02638889 | 0.003240741 | 0.05 | 0.004166667 | 0.1356481 | 0.006712963 | 0.01273148 | 0.07152778 | 0.03912037 | 0.01851852 | 0.01782407 | 0.000462963 | 0.04212963 | 0.01296296 | 0.007638889 | 0.001157407 | 0.2585648 | 0.02453704 | 0.02523148 | 0 | 0.03796296 | 0.004398148 | 0.003009259 | 0.01782407 | 0.07777778 | 0.001157407 | 0.05115741 | 0.2953704 | 0.09930556 | 0.3159722 | 0.0474537 | 0.01273148 | 0.006481481 | 0.0625 | 0.03148148 | 0.0662037 | 0.008333333 | 0.004166667 | 0.04861111 | 0.04050926 | 0.09930556 | 0.008564815 | 0.6270833 | 0.00625 | 0.006944444 | 0.006712963 | 0.05115741 | 0.07268519 | 0.3273148 | 0.02199074 | 0.01111111 | 0.1293981 | 0.01319444 | 0.03101852 | 0.03564815 | 0.0002314815 | 0.02013889 | 0.00162037 | 0.0125 | 0.0002314815 | 0.01828704 | 0.004166667 | 0.7148148 | 0.1847222 | 0.03541667 | 0.01643519 | 0.02453704 | 0.08449074 | 0.02592593 | 0.0009259259 | 0.003240741 | 0.01481481 | 0.04907407 | 0.01180556 | 0.005787037 | 0.002314815 | 0.008101852 | 0.05115741 | 0.05023148 | 0.02268519 | 0.05231481 | 0.03587963 | 0.1094907 | 0.02337963 | 0.02060185 | 0.03171296 | 0.008101852 | 0.000462963 | 0.004861111 | 0.04768519 | 0.01736111 | 0.0002314815 | 0.008333333 | 0.03680556 | 0.01481481 | 0.006481481 | 0.02731481 | 0.5368056 | 0.07314815 | 0.02893519 | 0.06273148 | 0.000462963 | 0 | 0.01134259 | 0.0599537 | 0.006712963 | 0 | 0.1085648 | 0.002083333 | 0.002083333 | 0.01134259 | 0.002777778 | 0.1527778 | 0.01597222 | 0.1800926 | 0.1523148 | 0.10625 | 0.007175926 | 0.2071759 | 0.00625 | 0.01087963 | 0.05625 | 0.19375 | 0.07592593 | 0.03726852 | 0.04212963 | 0.009259259 | 0.08726852 | 0.008564815 | 0.0337963 | 0.02361111 | 0.003240741 |
18 | spanish | 0.05358948 | 0.004044489 | 0.0950455 | 0.005055612 | 0.02426694 | 0.02325581 | 0.01617796 | 0.02426694 | 0.03336704 | 0.02426694 | 0.03538928 | 0.09403438 | 0.06471183 | 0 | 0.0364004 | 0.2679474 | 0.2537917 | 0.01921132 | 0.03336704 | 0.1183013 | 0.04246714 | 0.002022245 | 0.1344793 | 0.02224469 | 0.09302326 | 0.003033367 | 0.009100101 | 0.007077856 | 0.02022245 | 0.001011122 | 0.04550051 | 0.04044489 | 0.02426694 | 0.007077856 | 0.08897877 | 0.01415571 | 0.2649141 | 0.02325581 | 0.04853387 | 0.06875632 | 0 | 0.005055612 | 0.006066734 | 0.01617796 | 0.006066734 | 0.2264914 | 0.008088979 | 0.08695652 | 0.06875632 | 0.3852376 | 0.01617796 | 0.004044489 | 0.005055612 | 0.01617796 | 0.005055612 | 0.06673407 | 0.01112235 | 0.04145602 | 0.004044489 | 0.008088979 | 0.06875632 | 0.03538928 | 0.005055612 | 0.05662285 | 0.07886754 | 0.07077856 | 0.005055612 | 0.008088979 | 0.07482305 | 0.01213347 | 0.005055612 | 0.006066734 | 0.001011122 | 0.2780586 | 0.2770475 | 0.01314459 | 0 | 0.2578362 | 0.03538928 | 0.04044489 | 0.01718908 | 0.004044489 | 0.03235592 | 0.02325581 | 0.001011122 | 0.01617796 | 0.04448938 | 0.08291203 | 0.09908999 | 0.03235592 | 0.4924166 | 0.04651163 | 0 | 0.5672396 | 0.01112235 | 0.01314459 | 0.01011122 | 0.03943377 | 0.001011122 | 0.2608696 | 0.3650152 | 0.02123357 | 0.05358948 | 0.0182002 | 0 | 0.02527806 | 0.06066734 | 0.02628918 | 0.0182002 | 0.001011122 | 0.03134479 | 0.2457027 | 0.008088979 | 0.009100101 | 0.006066734 | 0.1233569 | 0.007077856 | 0.2537917 | 0.1314459 | 0.004044489 | 0.1051567 | 0.01112235 | 0.2163802 | 0.02527806 | 0.01314459 | 0.008088979 | 0.0677452 | 0.03741153 | 0.01718908 | 0 | 0.01516684 | 0.01213347 | 0.03235592 | 0.001011122 | 0.1081901 | 0.06268959 | 0.02527806 | 0 | 0.01112235 | 0.001011122 | 0.002022245 | 0.02426694 | 0.01617796 | 0.002022245 | 0.01617796 | 0.7421638 | 0.7371082 | 0.5217391 | 0.1425683 | 0.0677452 | 0.004044489 | 0.19818 | 0.01213347 | 0.247725 | 0.04246714 | 0.006066734 | 0.07280081 | 0 | 0.003033367 | 0.03033367 | 0.9201213 | 0.008088979 | 0.01011122 | 0.05257836 | 0.04448938 | 0.1678463 | 0.05358948 | 0.05561173 | 0.02831143 | 0.4115268 | 0.01011122 | 0.01415571 | 0.1365015 | 0.002022245 | 0.0586451 | 0 | 0.02325581 | 0.1274014 | 0.005055612 | 0.01314459 | 0.6845298 | 0.07785642 | 0.08998989 | 0.02426694 | 0.05257836 | 0.009100101 | 0.0182002 | 0.03336704 | 0.001011122 | 0.02932255 | 0.001011122 | 0.005055612 | 0.1284125 | 0.003033367 | 0.005055612 | 0.002022245 | 0.1223458 | 0.03134479 | 0.05561173 | 0.07583418 | 0.0182002 | 0.05662285 | 0.002022245 | 0.01921132 | 0.004044489 | 0.005055612 | 0.01921132 | 0.04954499 | 0.0273003 | 0 | 0.003033367 | 0.01718908 | 0.006066734 | 0.03437816 | 0.0586451 | 0.190091 | 0.06066734 | 0.01617796 | 0.007077856 | 0 | 0 | 0.03134479 | 0.07381193 | 0.004044489 | 0 | 0.4408493 | 0.008088979 | 0.009100101 | 0.0182002 | 0.003033367 | 0.04448938 | 0.003033367 | 0.04752275 | 0.08190091 | 0.2163802 | 0.008088979 | 0.2082912 | 0.02932255 | 0.004044489 | 0.01718908 | 0.2497472 | 0.03437816 | 0.273003 | 0.01415571 | 0.01213347 | 0.06471183 | 0.01112235 | 0.04145602 | 0.03336704 | 0.01921132 |
19 | thai | 0.01104613 | 0.0006497726 | 0.005198181 | 0.004548408 | 0.01169591 | 0.009096816 | 0.03638726 | 0.0006497726 | 0.005198181 | 0.00259909 | 0.217024 | 0.009096816 | 0.08836907 | 0.09876543 | 0.03508772 | 0.165692 | 0.08252112 | 0.008447044 | 0.1500975 | 0.01039636 | 0.2033788 | 0.0259909 | 0.1325536 | 0.2202729 | 0.1481481 | 0.0006497726 | 0.0591293 | 0.05003249 | 0 | 0.01364522 | 0.1461988 | 0.03443795 | 0.01364522 | 0 | 0.007797271 | 0.02209227 | 0.4951267 | 0.005198181 | 0.1851852 | 0.3612736 | 0.02274204 | 0.0006497726 | 0 | 0.01494477 | 0 | 0.2306693 | 0.009746589 | 0.474334 | 0.01494477 | 0.2319688 | 0.007147498 | 0.482781 | 0.009096816 | 0.005847953 | 0 | 0.1156595 | 0.122807 | 0.07147498 | 0 | 0.003898635 | 0.02858999 | 0.005847953 | 0.0006497726 | 0.06887589 | 0.08122157 | 0.05717999 | 0.3118908 | 0.05588044 | 0.007797271 | 0.001299545 | 0 | 0.001299545 | 0.007147498 | 0.09681611 | 0.1358025 | 0.02339181 | 0 | 0.005847953 | 0.003898635 | 0.03443795 | 0.00259909 | 0.0006497726 | 0.03573749 | 0.004548408 | 0.05782976 | 0.5406108 | 0.08901884 | 0.0006497726 | 0.03508772 | 0.01559454 | 0.8609487 | 0.01819363 | 0.001299545 | 0.6218324 | 0.3385315 | 0.006497726 | 0.01949318 | 0.0214425 | 0.001299545 | 0.3736192 | 0.2527615 | 0.03573749 | 0 | 0.005198181 | 0.01104613 | 0.05198181 | 0.04288499 | 0.006497726 | 0.001299545 | 0.0006497726 | 0.0545809 | 0.3833658 | 0 | 0.00259909 | 0.01949318 | 0.0545809 | 0.001949318 | 0.09551657 | 0.01169591 | 0.007147498 | 0.3424301 | 0.004548408 | 0.09811566 | 0.01364522 | 0.05003249 | 0.1195582 | 0.7238467 | 0.06887589 | 0.005198181 | 0.001949318 | 0.006497726 | 0.009096816 | 0.04418454 | 0 | 0.4106563 | 0.07927225 | 0.1111111 | 0.004548408 | 0.007147498 | 0.0006497726 | 0.00259909 | 0.0877193 | 0.009096816 | 0.1676413 | 0.006497726 | 0.6868096 | 0.08122157 | 0.454191 | 0.02469136 | 0.00259909 | 0.02988954 | 0.01234568 | 0.001299545 | 0.005198181 | 0.3703704 | 0.003248863 | 0.05782976 | 0.4061079 | 0 | 0.07212476 | 0.6250812 | 0.02469136 | 0.003248863 | 0.007147498 | 0.05393112 | 0.03183886 | 0.1137102 | 0.0545809 | 0.003898635 | 0.539961 | 0.03768681 | 0.01104613 | 0.4243015 | 0 | 0.1189084 | 0.05328135 | 0 | 0.001299545 | 0.0006497726 | 0.001299545 | 0.380117 | 1.063028 | 0 | 0.08966862 | 0.03053931 | 0.01234568 | 0.08836907 | 0.03248863 | 0.1604938 | 0.1585445 | 0 | 0.004548408 | 0.005198181 | 0.0259909 | 0.00259909 | 0.03573749 | 0.1903834 | 0.14295 | 0.06497726 | 0.001299545 | 0.001949318 | 0.1098116 | 0.00259909 | 0 | 0.3287849 | 0.005847953 | 0.02404159 | 0.02988954 | 0.0214425 | 0.03833658 | 0.02079272 | 0.04873294 | 0.0474334 | 0.03118908 | 0.07732294 | 0.5620533 | 0.08317089 | 0.007797271 | 0.014295 | 0 | 0.3164392 | 0.0402859 | 0.00259909 | 0.03508772 | 0.06887589 | 0.09616634 | 0.009746589 | 0.02534113 | 0.01169591 | 0.02664068 | 0.03573749 | 0.07862248 | 0.001949318 | 0.2690058 | 0.151397 | 0.005847953 | 0.2475634 | 0.07407407 | 0.007797271 | 0.001949318 | 0.1312541 | 0.008447044 | 0.03183886 | 0.00259909 | 0.001299545 | 0.04418454 | 0.004548408 | 0.0006497726 | 0.03573749 | 0.02988954 |
20 | vietnamese | 0.01454545 | 0.004848485 | 0.004848485 | 0.004848485 | 0.01333333 | 0.0169697 | 0.02424242 | 0.002424242 | 0.01090909 | 0.001212121 | 0.1963636 | 0.01090909 | 0.1018182 | 0.1539394 | 0.1333333 | 0.04848485 | 0.230303 | 0.006060606 | 0.08 | 0.01212121 | 0.08363636 | 0.01333333 | 0.09454545 | 0.12 | 0.07636364 | 0 | 0.07393939 | 0.1030303 | 0 | 0.01939394 | 0.3078788 | 0.01212121 | 0.01818182 | 0 | 0.002424242 | 0.002424242 | 0.2884848 | 0 | 0.2060606 | 0.3042424 | 0.04363636 | 0.001212121 | 0.001212121 | 0.01090909 | 0 | 0.1939394 | 0.01090909 | 0.4072727 | 0.06545455 | 0.2509091 | 0.01090909 | 0.07515152 | 0.01575758 | 0.02181818 | 0.001212121 | 0.08606061 | 0.1006061 | 0.07272727 | 0 | 0.01575758 | 0.008484848 | 0.002424242 | 0 | 0.04121212 | 0.1915152 | 0.01090909 | 0.03636364 | 0.04242424 | 0.00969697 | 0.001212121 | 0.007272727 | 0.001212121 | 0.006060606 | 0.08363636 | 0.1139394 | 0.007272727 | 0 | 0.006060606 | 0.004848485 | 0.0169697 | 0.01212121 | 0 | 0.0230303 | 0.008484848 | 0.04606061 | 0.5939394 | 0.04969697 | 0.001212121 | 0.07151515 | 0.006060606 | 0.6824242 | 0.008484848 | 0 | 0.6569697 | 0.2763636 | 0.004848485 | 0.02909091 | 0.01939394 | 0.001212121 | 0.2545455 | 0.3284848 | 0.008484848 | 0.006060606 | 0.002424242 | 0.07636364 | 0.04 | 0.07030303 | 0.01212121 | 0.003636364 | 0.001212121 | 0.07393939 | 0.3115152 | 0 | 0.002424242 | 0.007272727 | 0.1115152 | 0.002424242 | 0.0569697 | 0.02666667 | 0.003636364 | 0.3442424 | 0.004848485 | 0.07272727 | 0.007272727 | 0.1454545 | 0.04727273 | 0.4521212 | 0.03878788 | 0.002424242 | 0 | 0.04 | 0.03878788 | 0.05454545 | 0 | 0.07757576 | 0.1042424 | 0.2181818 | 0.007272727 | 0.00969697 | 0.001212121 | 0 | 0.05818182 | 0.003636364 | 0.2145455 | 0.002424242 | 0.64 | 0.05212121 | 0.5006061 | 0.0169697 | 0 | 0.03393939 | 0.007272727 | 0.001212121 | 0.006060606 | 0.07878788 | 0 | 0.02060606 | 0.2060606 | 0 | 0.04121212 | 0.6048485 | 0.03030303 | 0.002424242 | 0.007272727 | 0.2012121 | 0.01818182 | 0.09333333 | 0.05454545 | 0 | 0.2133333 | 0.02424242 | 0.01454545 | 0.5781818 | 0 | 0.09575758 | 0.03272727 | 0 | 0 | 0 | 0 | 0.4848485 | 1.140606 | 0.01333333 | 0.1212121 | 0.02424242 | 0.01333333 | 0.08242424 | 0.04484848 | 0.1781818 | 0.1915152 | 0 | 0.003636364 | 0.002424242 | 0.03272727 | 0.01939394 | 0.05333333 | 0.1636364 | 0.06060606 | 0.05333333 | 0 | 0.006060606 | 0.07878788 | 0.006060606 | 0.001212121 | 0.2860606 | 0.004848485 | 0.00969697 | 0.0230303 | 0.03757576 | 0.07636364 | 0.00969697 | 0.08 | 0.07878788 | 0.07636364 | 0.06181818 | 0.6618182 | 0.03151515 | 0.02424242 | 0.004848485 | 0 | 0.1624242 | 0.02181818 | 0 | 0.04484848 | 0.06666667 | 0.07030303 | 0.003636364 | 0.01575758 | 0.007272727 | 0.0230303 | 0.04 | 0.006060606 | 0.004848485 | 0.2072727 | 0.2363636 | 0.02909091 | 0.3612121 | 0.05212121 | 0.00969697 | 0 | 0.1709091 | 0.01333333 | 0.04 | 0.001212121 | 0.002424242 | 0.08363636 | 0.002424242 | 0.008484848 | 0.006060606 | 0.003636364 |
ingredientsDTM_train_korea <- ingredientsDTM_train[ingredientsDTM_train$cuisine == "korean",]
head(ingredientsDTM_train_korea)
all-purpos | allspic | almond | and | appl | avocado | babi | bacon | bake | balsam | basil | bay | bean | beansprout | beef | bell | black | boil | boneless | bread | breast | broccoli | broth | brown | butter | buttermilk | cabbag | canola | caper | cardamom | carrot | cayenn | celeri | cheddar | chees | cherri | chicken | chickpea | chile | chili | chines | chip | chipotl | chive | chocol | chop | cider | cilantro | cinnamon | clove | coars | coconut | cold | condens | confection | cook | coriand | corn | cornmeal | crack | cream | crumb | crumbl | crush | cucumb | cumin | curri | dark | dice | dijon | dill | dough | dress | dri | egg | eggplant | enchilada | extra-virgin | extract | fat | fennel | feta | fillet | fine | firm | fish | flake | flat | flour | free | fresh | frozen | garam | garlic | ginger | golden | granul | grate | greek | green | ground | halv | ham | heavi | hoisin | honey | hot | ice | italian | jack | jalapeno | juic | kalamata | kernel | ketchup | kosher | lamb | larg | leaf | lean | leav | leek | lemon | less | lettuc | light | lime | low | low-fat | masala | mayonais | meat | medium | mexican | milk | minc | mint | mirin | mix | monterey | mozzarella | mushroom | mustard | noodl | nutmeg | oil | oliv | onion | orang | oregano | oyster | paprika | parmesan | parsley | past | pasta | pea | peanut | pecan | peel | pepper | peppercorn | plain | plum | pork | potato | powder | purpl | raisin | red | reduc | rib | rice | ricotta | roast | root | rosemari | saffron | sage | salsa | salt | sauc | sausag | scallion | sea | season | seed | serrano | sesam | shallot | sharp | shell | sherri | shiitak | shoulder | shred | shrimp | skinless | slice | smoke | soda | sodium | soup | sour | soy | spaghetti | spinach | spray | sprig | spring | squash | starch | steak | stick | stock | sugar | sweet | sweeten | syrup | taco | thai | thigh | thyme | toast | tofu | tomato | tortilla | tumer | turkey | turmer | unsalt | unsweeten | vanilla | veget | vinegar | warm | water | wedg | wheat | whip | white | whole | wine | worcestershir | yeast | yellow | yogurt | yolk | zest | zucchini | cuisine | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
67 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | korean |
89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | korean |
105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | korean |
110 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | korean |
141 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | korean |
158 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | korean |
ingredients_korea_sum <- apply(ingredientsDTM_train_korea[,-251], 2, sum)
ingredients_korea_mean <- apply(ingredientsDTM_train_korea[,-251], 2, mean)
ingredients_korea_sum <- ingredients_korea_sum[order(ingredients_korea_sum, decreasing=T)]
ingredients_korea_mean <- ingredients_korea_mean[order(ingredients_korea_mean, decreasing=T)]
barplot(ingredients_korea_sum[1:30], las=2)
names(ingredients_korea_sum)
barplot(ingredients_korea_mean[1:30], las=2)
cartModelFit <- rpart(cuisine ~ ., data = train_data, method = "class")
cartModelFit
n= 23871 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 23871 19168 italian (0.012 0.02 0.039 0.067 0.019 0.067 0.03 0.075 0.017 0.2 0.013 0.036 0.021 0.16 0.021 0.012 0.11 0.025 0.039 0.021) 2) tortilla< 0.5 22273 17583 italian (0.013 0.022 0.042 0.072 0.02 0.071 0.031 0.081 0.018 0.21 0.014 0.038 0.022 0.1 0.022 0.013 0.12 0.026 0.041 0.022) 4) parmesan>=0.5 1714 277 italian (0.0053 0.0018 0.023 0.00058 0.0012 0.037 0.015 0.0018 0.0018 0.84 0.00058 0.0035 0 0.016 0.0018 0.00058 0.047 0.0041 0.00058 0) * 5) parmesan< 0.5 20559 17306 italian (0.013 0.023 0.043 0.078 0.022 0.074 0.033 0.087 0.019 0.16 0.015 0.041 0.024 0.11 0.024 0.014 0.12 0.028 0.044 0.024) 10) soy>=0.5 2731 1525 chinese (0.00037 0.00073 0.0026 0.44 0.062 0.0015 0.0015 0.0084 0.0011 0.004 0.019 0.17 0.11 0.0088 0.0011 0.00073 0.0092 0.0011 0.11 0.048) * 11) soy< 0.5 17828 14586 italian (0.015 0.027 0.049 0.022 0.016 0.085 0.038 0.099 0.022 0.18 0.015 0.022 0.01 0.13 0.027 0.016 0.14 0.033 0.035 0.02) 22) masala>=0.5 614 41 indian (0.0016 0.0033 0 0.0016 0 0 0 0.93 0 0.0016 0.0016 0.047 0 0 0.0065 0.0016 0.0016 0 0 0) * 23) masala< 0.5 17214 13973 italian (0.016 0.028 0.051 0.023 0.016 0.088 0.039 0.07 0.023 0.19 0.015 0.021 0.01 0.13 0.028 0.017 0.14 0.034 0.036 0.021) 46) cilantro< 0.5 15174 11953 italian (0.015 0.031 0.057 0.024 0.018 0.1 0.044 0.055 0.026 0.21 0.016 0.023 0.012 0.092 0.022 0.019 0.16 0.035 0.022 0.015) 92) oliv>=0.5 4373 2479 italian (0.01 0.011 0.046 0.0037 0.0059 0.096 0.1 0.023 0.0073 0.43 0.0048 0.0032 0.0016 0.067 0.043 0.0053 0.048 0.081 0.0059 0.0027) * 93) oliv< 0.5 10801 8557 southern_us (0.017 0.039 0.062 0.032 0.023 0.1 0.021 0.068 0.033 0.12 0.021 0.031 0.016 0.1 0.013 0.024 0.21 0.016 0.029 0.02) 186) cumin< 0.5 10103 7888 southern_us (0.018 0.042 0.064 0.034 0.025 0.11 0.021 0.045 0.036 0.13 0.021 0.033 0.017 0.083 0.0092 0.026 0.22 0.017 0.03 0.021) * 187) cumin>=0.5 698 421 indian (0.0072 0.0043 0.029 0 0.0029 0.0086 0.01 0.4 0 0.0014 0.016 0.011 0 0.38 0.073 0 0.042 0.0057 0.014 0.0014) * 47) cilantro>=0.5 2040 1150 mexican (0.021 0.00098 0.0064 0.015 0.00098 0.0034 0.00098 0.18 0.00098 0.0098 0.0054 0.0059 0.0025 0.44 0.075 0.002 0.013 0.023 0.13 0.066) * 3) tortilla>=0.5 1598 70 mexican (0.00063 0 0.0019 0.005 0.0013 0 0.0031 0.0038 0 0.0081 0.0019 0.00063 0.005 0.96 0 0 0.0025 0.0025 0.0056 0.0019) *
prp(cartModelFit)
tortilla : 옥수수
paramesan : 치즈
soy : 대두
mirin : 미림 (일본 술의 한 종류, 조미료)
masala (향신료)
- Garam masala (from Hindi: गरम मसाला, garam ("hot") and masala (a mixture of spices)) is a blend of ground spices common in North Indian and other South Asian cuisines.[1] It is used alone or with other seasonings. The word garam refers to "heat" in the Ayurvedic sense of the word, meaning "to heat the body" as these spices, in the Ayurvedic system of medicine, elevate body temperature.
cilantro : 고수 (미나리과 살이풀)
oliv : 올리브
cartPredict <- predict(cartModelFit, newdata = vaild_data, type = "class")
head(cartPredict)
cartCM <- confusionMatrix(cartPredict, vaild_data$cuisine)
cartCM
Confusion Matrix and Statistics Reference Prediction brazilian british cajun_creole chinese filipino french greek brazilian 0 0 0 0 0 0 0 british 0 0 0 0 0 0 0 cajun_creole 0 0 0 0 0 0 0 chinese 1 3 5 821 118 3 2 filipino 0 0 0 0 0 0 0 french 0 0 0 0 0 0 0 greek 0 0 0 0 0 0 0 indian 0 3 17 1 1 2 10 irish 0 0 0 0 0 0 0 italian 55 29 163 14 8 369 307 jamaican 0 0 0 0 0 0 0 japanese 0 0 0 0 0 0 0 korean 0 0 0 0 0 0 0 mexican 37 2 12 26 6 7 7 moroccan 0 0 0 0 0 0 0 russian 0 0 0 0 0 0 0 southern_us 93 284 421 207 169 677 144 spanish 0 0 0 0 0 0 0 thai 0 0 0 0 0 0 0 vietnamese 0 0 0 0 0 0 0 Reference Prediction indian irish italian jamaican japanese korean mexican moroccan brazilian 0 0 0 0 0 0 0 0 british 0 0 0 0 0 0 0 0 cajun_creole 0 0 0 0 0 0 0 0 chinese 10 4 8 28 301 218 12 1 filipino 0 0 0 0 0 0 0 0 french 0 0 0 0 0 0 0 0 greek 0 0 0 0 0 0 0 0 indian 547 2 10 8 31 0 174 26 irish 0 0 0 0 0 0 0 0 italian 82 29 2209 23 17 7 226 129 jamaican 0 0 0 0 0 0 0 0 japanese 0 0 0 0 0 0 0 0 korean 0 0 0 0 0 0 0 0 mexican 242 3 23 16 16 2 1597 123 moroccan 0 0 0 0 0 0 0 0 russian 0 0 0 0 0 0 0 0 southern_us 320 228 885 135 204 105 566 49 spanish 0 0 0 0 0 0 0 0 thai 0 0 0 0 0 0 0 0 vietnamese 0 0 0 0 0 0 0 0 Reference Prediction russian southern_us spanish thai vietnamese brazilian 0 0 0 0 0 british 0 0 0 0 0 cajun_creole 0 0 0 0 0 chinese 1 10 1 187 99 filipino 0 0 0 0 0 french 0 0 0 0 0 greek 0 0 0 0 0 indian 2 21 5 14 1 irish 0 0 0 0 0 italian 36 202 237 7 3 jamaican 0 0 0 0 0 japanese 0 0 0 0 0 korean 0 0 0 0 0 mexican 5 30 34 201 95 moroccan 0 0 0 0 0 russian 0 0 0 0 0 southern_us 151 1465 118 206 132 spanish 0 0 0 0 0 thai 0 0 0 0 0 vietnamese 0 0 0 0 0 Overall Statistics Accuracy : 0.4175 95% CI : (0.4098, 0.4252) No Information Rate : 0.1971 P-Value [Acc > NIR] : < 2.2e-16 Kappa : 0.3277 Mcnemar's Test P-Value : NA Statistics by Class: Class: brazilian Class: british Class: cajun_creole Sensitivity 0.0000 0.00000 0.00000 Specificity 1.0000 1.00000 1.00000 Pos Pred Value NaN NaN NaN Neg Pred Value 0.9883 0.97982 0.96114 Prevalence 0.0117 0.02018 0.03886 Detection Rate 0.0000 0.00000 0.00000 Detection Prevalence 0.0000 0.00000 0.00000 Balanced Accuracy 0.5000 0.50000 0.50000 Class: chinese Class: filipino Class: french Class: greek Sensitivity 0.76801 0.00000 0.00000 0.00000 Specificity 0.93178 1.00000 1.00000 1.00000 Pos Pred Value 0.44790 NaN NaN NaN Neg Pred Value 0.98237 0.98101 0.93347 0.97045 Prevalence 0.06722 0.01899 0.06653 0.02955 Detection Rate 0.05163 0.00000 0.00000 0.00000 Detection Prevalence 0.11526 0.00000 0.00000 0.00000 Balanced Accuracy 0.84989 0.50000 0.50000 0.50000 Class: indian Class: irish Class: italian Class: jamaican Sensitivity 0.45545 0.00000 0.7046 0.00000 Specificity 0.97769 1.00000 0.8478 1.00000 Pos Pred Value 0.62514 NaN 0.5320 NaN Neg Pred Value 0.95648 0.98327 0.9212 0.98679 Prevalence 0.07552 0.01673 0.1971 0.01321 Detection Rate 0.03440 0.00000 0.1389 0.00000 Detection Prevalence 0.05502 0.00000 0.2611 0.00000 Balanced Accuracy 0.71657 0.50000 0.7762 0.50000 Class: japanese Class: korean Class: mexican Sensitivity 0.00000 0.00000 0.6202 Specificity 1.00000 1.00000 0.9334 Pos Pred Value NaN NaN 0.6429 Neg Pred Value 0.96422 0.97912 0.9271 Prevalence 0.03578 0.02088 0.1619 Detection Rate 0.00000 0.00000 0.1004 Detection Prevalence 0.00000 0.00000 0.1562 Balanced Accuracy 0.50000 0.50000 0.7768 Class: moroccan Class: russian Class: southern_us Sensitivity 0.00000 0.00000 0.84780 Specificity 1.00000 1.00000 0.64063 Pos Pred Value NaN NaN 0.22336 Neg Pred Value 0.97937 0.98774 0.97185 Prevalence 0.02063 0.01226 0.10866 Detection Rate 0.00000 0.00000 0.09212 Detection Prevalence 0.00000 0.00000 0.41244 Balanced Accuracy 0.50000 0.50000 0.74422 Class: spanish Class: thai Class: vietnamese Sensitivity 0.00000 0.00000 0.00000 Specificity 1.00000 1.00000 1.00000 Pos Pred Value NaN NaN NaN Neg Pred Value 0.97516 0.96133 0.97925 Prevalence 0.02484 0.03867 0.02075 Detection Rate 0.00000 0.00000 0.00000 Detection Prevalence 0.00000 0.00000 0.00000 Balanced Accuracy 0.50000 0.50000 0.50000
install.packages("rJava", dependencies=T, repos='http://cran.rstudio.com/')
package 'rJava' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages
install.packages("AUC", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("xgboost", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("RSofia", dependencies=T, repos='http://cran.rstudio.com/')
install.packages("extraTrees", dependencies=T, repos='http://cran.rstudio.com/')
package 'AUC' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages
also installing the dependencies 'jsonlite', 'stringi', 'rjson', 'htmlwidgets', 'rstudioapi', 'stringr', 'visNetwork', 'lmtest', 'DiagrammeR', 'Ckmeans.1d.dp', 'vcd'
package 'jsonlite' successfully unpacked and MD5 sums checked
Warning message: : cannot remove prior installation of package 'jsonlite'
package 'stringi' successfully unpacked and MD5 sums checked package 'rjson' successfully unpacked and MD5 sums checked package 'htmlwidgets' successfully unpacked and MD5 sums checked package 'rstudioapi' successfully unpacked and MD5 sums checked package 'stringr' successfully unpacked and MD5 sums checked package 'visNetwork' successfully unpacked and MD5 sums checked package 'lmtest' successfully unpacked and MD5 sums checked package 'DiagrammeR' successfully unpacked and MD5 sums checked package 'Ckmeans.1d.dp' successfully unpacked and MD5 sums checked package 'vcd' successfully unpacked and MD5 sums checked package 'xgboost' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages
also installing the dependency 'RUnit'
package 'RUnit' successfully unpacked and MD5 sums checked package 'RSofia' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages package 'extraTrees' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages
library(rJava)
library(extraTrees)
library(xgboost)
library(RSofia)
library(AUC)
cartPredict <- predict(cartModelFit, newdata = vaild_data, type = "prob")
head(cartPredict)
brazilian | british | cajun_creole | chinese | filipino | french | greek | indian | irish | italian | jamaican | japanese | korean | mexican | moroccan | russian | southern_us | spanish | thai | vietnamese | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | 0.001628664 | 0.003257329 | 0.000000000 | 0.001628664 | 0.000000000 | 0.000000000 | 0.000000000 | 0.933224756 | 0.000000000 | 0.001628664 | 0.001628664 | 0.047231270 | 0.000000000 | 0.000000000 | 0.006514658 | 0.001628664 | 0.001628664 | 0.000000000 | 0.000000000 | 0.000000000 |
7 | 0.0205882353 | 0.0009803922 | 0.0063725490 | 0.0147058824 | 0.0009803922 | 0.0034313725 | 0.0009803922 | 0.1779411765 | 0.0009803922 | 0.0098039216 | 0.0053921569 | 0.0058823529 | 0.0024509804 | 0.4362745098 | 0.0745098039 | 0.0019607843 | 0.0127450980 | 0.0230392157 | 0.1348039216 | 0.0661764706 |
9 | 0.0006257822 | 0.0000000000 | 0.0018773467 | 0.0050062578 | 0.0012515645 | 0.0000000000 | 0.0031289111 | 0.0037546934 | 0.0000000000 | 0.0081351690 | 0.0018773467 | 0.0006257822 | 0.0050062578 | 0.9561952441 | 0.0000000000 | 0.0000000000 | 0.0025031289 | 0.0025031289 | 0.0056320401 | 0.0018773467 |
12 | 0.0003661662 | 0.0007323325 | 0.0025631637 | 0.4415964848 | 0.0618820945 | 0.0014646650 | 0.0014646650 | 0.0084218235 | 0.0010984987 | 0.0040278286 | 0.0190406445 | 0.1662394727 | 0.1135115342 | 0.0087879897 | 0.0010984987 | 0.0007323325 | 0.0091541560 | 0.0010984987 | 0.1091175394 | 0.0476016111 |
14 | 0.0205882353 | 0.0009803922 | 0.0063725490 | 0.0147058824 | 0.0009803922 | 0.0034313725 | 0.0009803922 | 0.1779411765 | 0.0009803922 | 0.0098039216 | 0.0053921569 | 0.0058823529 | 0.0024509804 | 0.4362745098 | 0.0745098039 | 0.0019607843 | 0.0127450980 | 0.0230392157 | 0.1348039216 | 0.0661764706 |
15 | 0.0052508751 | 0.0017502917 | 0.0233372229 | 0.0005834306 | 0.0011668611 | 0.0367561260 | 0.0145857643 | 0.0017502917 | 0.0017502917 | 0.8383897316 | 0.0005834306 | 0.0035005834 | 0.0000000000 | 0.0163360560 | 0.0017502917 | 0.0005834306 | 0.0472578763 | 0.0040840140 | 0.0005834306 | 0.0000000000 |
install.packages("pROC", dependencies=T, repos='http://cran.rstudio.com/')
also installing the dependencies 'misc3d', 'rgl', 'multicool', 'ks', 'logcondens', 'doParallel'
package 'misc3d' successfully unpacked and MD5 sums checked package 'rgl' successfully unpacked and MD5 sums checked package 'multicool' successfully unpacked and MD5 sums checked package 'ks' successfully unpacked and MD5 sums checked package 'logcondens' successfully unpacked and MD5 sums checked package 'doParallel' successfully unpacked and MD5 sums checked package 'pROC' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\syleeie\RtmpKcFXpv\downloaded_packages
library(pROC)
Type 'citation("pROC")' for a citation. Attaching package: 'pROC' The following objects are masked from 'package:AUC': auc, roc The following objects are masked from 'package:stats': cov, smooth, var
str(vaild_data$cuisine)
Factor w/ 20 levels "brazilian","british",..: 8 18 14 4 14 10 2 10 19 10 ...
levels(vaild_data$cuisine)
cartPredict_Prob <- apply(cartPredict, 1, which.max)
head(cartPredict_Prob)
multiclass.roc(response=vaild_data$cuisine, predictor=cartPredict_Prob, levels=levels(vaild_data$cuisine),percent=TRUE)
Call: multiclass.roc.default(response = vaild_data$cuisine, predictor = cartPredict_Prob, levels = levels(vaild_data$cuisine), percent = TRUE) Data: cartPredict_Prob with 20 levels of vaild_data$cuisine: brazilian, british, cajun_creole, chinese, filipino, french, greek, indian, irish, italian, jamaican, japanese, korean, mexican, moroccan, russian, southern_us, spanish, thai, vietnamese. Multi-class area under the curve: 65.14%
cartPredict_test <- predict(cartModelFit, newdata = test_data, type = "class")
head(cartPredict_test)
head(test$id)
test_predict <- data.frame(id = test$id, cuisine = cartPredict_test)
head(test_predict)
id | cuisine | |
---|---|---|
39775 | 18009 | southern_us |
39776 | 28583 | southern_us |
39777 | 41580 | italian |
39778 | 29752 | southern_us |
39779 | 35687 | italian |
39780 | 38527 | southern_us |
submission <- left_join(sample_sub, test_predict, by="id")
head(submission)
id | cuisine.x | cuisine.y | |
---|---|---|---|
1 | 35203 | italian | southern_us |
2 | 17600 | italian | mexican |
3 | 35200 | italian | southern_us |
4 | 17602 | italian | mexican |
5 | 17605 | italian | indian |
6 | 17604 | italian | chinese |
submission <- submission[,-2]
head(submission)
id | cuisine.y | |
---|---|---|
1 | 35203 | southern_us |
2 | 17600 | mexican |
3 | 35200 | southern_us |
4 | 17602 | mexican |
5 | 17605 | indian |
6 | 17604 | chinese |
colnames(submission) <- c("id", "cuisine")
head(submission)
id | cuisine | |
---|---|---|
1 | 35203 | southern_us |
2 | 17600 | mexican |
3 | 35200 | southern_us |
4 | 17602 | mexican |
5 | 17605 | indian |
6 | 17604 | chinese |
write.csv(submission, file = 'cart_submission.csv', row.names = F)
library(extraTrees)
extraTreesModelFit <- extraTrees(train_data[,-251], train_data[,251], ntree=100, numThreads=4)
extraTreesModelFit
ExtraTrees: - # of trees: 100 - node size: 1 - # of dim: 250 - # of tries: 15 - type: factor (classification) - multi-task: no
vaild_label_extra <- predict(extraTreesModelFit, vaild_data[,-251], probability=T)
Error in .jcall(et$jobject, "Lorg/extratrees/data/Matrix;", "getAllValues", : java.lang.NullPointerException
head(vaild_label_extra)
brazilian | british | cajun_creole | chinese | filipino | french | greek | indian | irish | italian | jamaican | japanese | korean | mexican | moroccan | russian | southern_us | spanish | thai | vietnamese |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.89 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.05 | 0.00 | 0.01 | 0.03 | 0.00 | 0.00 | 0.00 |
0.12 | 0.02 | 0.07 | 0.02 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.14 | 0.00 | 0.01 | 0.01 | 0.43 | 0.07 | 0.00 | 0.04 | 0.03 | 0.02 | 0.00 |
0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.98 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 |
0.00 | 0.00 | 0.00 | 0.89 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.04 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.02 |
0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | 0.01 | 0.06 | 0.00 | 0.00 | 0.03 | 0.01 | 0.00 | 0.76 | 0.10 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 |
0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | 0.08 | 0.65 | 0.00 | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 | 0.19 | 0.00 | 0.00 | 0.00 |
vaild_label_extra2 <- predict(extraTreesModelFit, vaild_data[,-251], probability=F)
Error in .jarray(m): java.lang.OutOfMemoryError: Java heap space
extraCM <- confusionMatrix(vaild_label_extra2, vaild_data$cuisine)
extraCM
Confusion Matrix and Statistics Reference Prediction brazilian british cajun_creole chinese filipino french greek brazilian 81 2 1 0 7 1 0 british 0 98 1 1 6 10 3 cajun_creole 5 1 388 1 1 12 3 chinese 0 2 3 913 46 4 0 filipino 1 0 0 8 154 1 2 french 6 45 19 8 7 535 16 greek 0 3 2 1 1 7 298 indian 6 11 3 4 11 2 13 irish 0 18 0 0 2 10 1 italian 31 38 58 15 8 307 103 jamaican 2 2 1 2 0 2 0 japanese 0 1 3 38 1 4 1 korean 0 1 1 20 1 0 0 mexican 29 7 41 11 12 20 9 moroccan 1 2 2 0 0 6 7 russian 2 2 2 0 2 4 0 southern_us 14 86 88 22 25 121 11 spanish 1 1 5 1 1 11 3 thai 7 0 0 18 16 1 0 vietnamese 0 1 0 6 1 0 0 Reference Prediction indian irish italian jamaican japanese korean mexican moroccan brazilian 1 2 0 3 1 0 8 0 british 1 15 15 1 2 3 3 1 cajun_creole 2 2 10 4 4 1 7 4 chinese 5 1 5 3 105 63 1 3 filipino 1 1 3 2 3 5 1 0 french 8 35 124 1 16 0 20 9 greek 8 0 33 0 0 0 4 4 indian 1070 6 7 12 56 2 14 37 irish 1 98 4 0 1 0 1 1 italian 14 28 2738 8 14 1 52 24 jamaican 6 0 3 110 0 0 4 0 japanese 9 3 4 2 308 33 9 0 korean 0 1 0 0 20 200 4 1 mexican 35 5 66 25 11 7 2368 20 moroccan 16 1 5 3 0 0 7 212 russian 2 2 7 1 2 1 0 0 southern_us 7 65 92 29 16 5 60 8 spanish 2 1 15 2 0 0 8 2 thai 12 0 1 4 8 7 2 1 vietnamese 1 0 3 0 2 4 2 1 Reference Prediction russian southern_us spanish thai vietnamese brazilian 0 4 2 4 2 british 5 11 2 0 0 cajun_creole 0 75 5 2 0 chinese 1 10 2 49 42 filipino 1 5 2 0 2 french 36 73 29 1 1 greek 6 5 5 0 0 indian 6 15 5 37 11 irish 2 9 0 0 0 italian 39 145 110 9 2 jamaican 0 2 0 1 1 japanese 1 5 0 3 7 korean 0 1 0 3 2 mexican 13 87 56 13 8 moroccan 0 6 3 1 0 russian 56 6 2 0 0 southern_us 27 1256 20 3 4 spanish 2 10 150 0 0 thai 0 2 2 464 92 vietnamese 0 1 0 25 156 Overall Statistics Accuracy : 0.7328 95% CI : (0.7258, 0.7396) No Information Rate : 0.1971 P-Value [Acc > NIR] : < 2.2e-16 Kappa : 0.6993 Mcnemar's Test P-Value : NA Statistics by Class: Class: brazilian Class: british Class: cajun_creole Sensitivity 0.435484 0.305296 0.62783 Specificity 0.997582 0.994866 0.99091 Pos Pred Value 0.680672 0.550562 0.73624 Neg Pred Value 0.993348 0.985819 0.98504 Prevalence 0.011696 0.020185 0.03886 Detection Rate 0.005093 0.006162 0.02440 Detection Prevalence 0.007483 0.011193 0.03314 Balanced Accuracy 0.716533 0.650081 0.80937 Class: chinese Class: filipino Class: french Class: greek Sensitivity 0.85407 0.509934 0.50567 0.63404 Specificity 0.97674 0.997564 0.96942 0.99488 Pos Pred Value 0.72576 0.802083 0.54095 0.79045 Neg Pred Value 0.98935 0.990580 0.96493 0.98892 Prevalence 0.06722 0.018990 0.06653 0.02955 Detection Rate 0.05741 0.009684 0.03364 0.01874 Detection Prevalence 0.07910 0.012073 0.06219 0.02371 Balanced Accuracy 0.91541 0.753749 0.73754 0.81446 Class: indian Class: irish Class: italian Class: jamaican Sensitivity 0.89092 0.368421 0.8734 0.523810 Specificity 0.98245 0.996802 0.9212 0.998343 Pos Pred Value 0.80572 0.662162 0.7313 0.808824 Neg Pred Value 0.99101 0.989337 0.9673 0.993658 Prevalence 0.07552 0.016726 0.1971 0.013205 Detection Rate 0.06728 0.006162 0.1722 0.006917 Detection Prevalence 0.08351 0.009306 0.2354 0.008552 Balanced Accuracy 0.93669 0.682612 0.8973 0.761076 Class: japanese Class: korean Class: mexican Sensitivity 0.54130 0.60241 0.9196 Specificity 0.99191 0.99647 0.9644 Pos Pred Value 0.71296 0.78431 0.8329 Neg Pred Value 0.98313 0.99156 0.9842 Prevalence 0.03578 0.02088 0.1619 Detection Rate 0.01937 0.01258 0.1489 Detection Prevalence 0.02716 0.01603 0.1788 Balanced Accuracy 0.76661 0.79944 0.9420 Class: moroccan Class: russian Class: southern_us Sensitivity 0.64634 0.287179 0.72685 Specificity 0.99615 0.997772 0.95041 Pos Pred Value 0.77941 0.615385 0.64114 Neg Pred Value 0.99258 0.991209 0.96615 Prevalence 0.02063 0.012262 0.10866 Detection Rate 0.01333 0.003521 0.07898 Detection Prevalence 0.01710 0.005722 0.12318 Balanced Accuracy 0.82124 0.642476 0.83863 Class: spanish Class: thai Class: vietnamese Sensitivity 0.379747 0.75447 0.472727 Specificity 0.995809 0.98868 0.996982 Pos Pred Value 0.697674 0.72841 0.768473 Neg Pred Value 0.984383 0.99011 0.988917 Prevalence 0.024838 0.03867 0.020751 Detection Rate 0.009432 0.02918 0.009809 Detection Prevalence 0.013519 0.04006 0.012765 Balanced Accuracy 0.687778 0.87158 0.734855
extraPredict_Prob <- apply(vaild_label_extra, 1, which.max)
multiclass.roc(response=vaild_data$cuisine, predictor=extraPredict_Prob, levels=levels(vaild_data$cuisine),percent=TRUE)
Call: multiclass.roc.default(response = vaild_data$cuisine, predictor = extraPredict_Prob, levels = levels(vaild_data$cuisine), percent = TRUE) Data: extraPredict_Prob with 20 levels of vaild_data$cuisine: brazilian, british, cajun_creole, chinese, filipino, french, greek, indian, irish, italian, jamaican, japanese, korean, mexican, moroccan, russian, southern_us, spanish, thai, vietnamese. Multi-class area under the curve: 73.82%
extraPredict_test <- predict(extraTreesModelFit, newdata = test_data[,-251], probability=F)
head(extraPredict_test)
extra_test_predict <- data.frame(id = test$id, cuisine = extraPredict_test)
head(extra_test_predict)
id | cuisine | |
---|---|---|
1 | 18009 | southern_us |
2 | 28583 | southern_us |
3 | 41580 | italian |
4 | 29752 | cajun_creole |
5 | 35687 | italian |
6 | 38527 | southern_us |
head(test_predict)
id | cuisine | |
---|---|---|
39775 | 18009 | southern_us |
39776 | 28583 | southern_us |
39777 | 41580 | italian |
39778 | 29752 | southern_us |
39779 | 35687 | italian |
39780 | 38527 | southern_us |
submission <- left_join(sample_sub, extra_test_predict, by="id")
submission <- submission[,-2]
colnames(submission) <- c("id", "cuisine")
write.csv(submission, file = 'extra_submission.csv', row.names = F)