# Start pylab inline mode, so figures will appear in the notebook
%matplotlib inline
Machine Learning is about building programs with tunable parameters that are adjusted automatically so as to improve their behavior by adapting to previously seen data.
Machine Learning can be considered a subfield of Artificial Intelligence since those algorithms can be seen as building blocks to make computers learn to behave more intelligently by somehow generalizing rather that just storing and retrieving data items like a database system would do.
We'll take a look at two very simple machine learning tasks here. The first is a classification task: the figure shows a collection of two-dimensional data, colored according to two different class labels. A classification algorithm may be used to draw a dividing boundary between the two clusters of points:
# Import the example plot from the figures directory
from figures import plot_sgd_separator
plot_sgd_separator()
By drawing this separating line, we have learned a model which can generalize to new data: if you were to drop another point onto the plane which is unlabeled, this algorithm could now predict whether it's a blue or a red point.
The next simple task we'll look at is a regression task: a simple best-fit line to a set of data:
from figures import plot_linear_regression
plot_linear_regression()
Again, this is an example of fitting a model to data, but our focus here is that the model can make generalizations about new data. The model has been learned from the training data, and can be used to predict the result of test data: here, we might be given an x-value, and the model would allow us to predict the y value.
Most machine learning algorithms implemented in scikit-learn expect data to be stored in a
two-dimensional array or matrix. The arrays can be
either numpy
arrays, or in some cases scipy.sparse
matrices.
The size of the array is expected to be [n_samples, n_features]
The number of features must be fixed in advance. However it can be very high dimensional
(e.g. millions of features) with most of them being zeros for a given sample. This is a case
where scipy.sparse
matrices can be useful, in that they are
much more memory-efficient than numpy arrays.
As an example of a simple dataset, we're going to take a look at the iris data stored by scikit-learn. The data consists of measurements of three different species of irises. There are three species of iris in the dataset, which we can picture here:
from IPython.core.display import Image, display
display(Image(filename='figures/iris_setosa.jpg'))
print("Iris Setosa\n")
display(Image(filename='figures/iris_versicolor.jpg'))
print("Iris Versicolor\n")
display(Image(filename='figures/iris_virginica.jpg'))
print("Iris Virginica")
If we want to design an algorithm to recognize iris species, what might the data be?
Remember: we need a 2D array of size [n_samples x n_features]
.
What would the n_samples
refer to?
What might the n_features
refer to?
Remember that there must be a fixed number of features for each sample, and feature
number i
must be a similar kind of quantity for each sample.
Scikit-learn has a very straightforward set of data on these iris species. The data consist of the following:
Features in the Iris dataset:
Target classes to predict:
scikit-learn
embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:
from sklearn.datasets import load_iris
iris = load_iris()
The features of each sample flower are stored in the data
attribute of the dataset:
n_samples, n_features = iris.data.shape
print(n_samples)
print(n_features)
print(iris.data[0])
The information about the class of each sample is stored in the target
attribute of the dataset:
print(iris.data.shape)
print(iris.target.shape)
print(iris.target)
The names of the classes are stored in the last attribute, namely target_names
:
print(iris.target_names)
This data is four dimensional, but we can visualize two of the dimensions at a time using a simple scatter-plot. Again, we'll start by enabling matplotlib inline mode:
from matplotlib import pyplot as plt
x_index = 0
y_index = 1
# this formatter will label the colorbar with the correct target names
formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])
plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.xlabel(iris.feature_names[x_index])
plt.ylabel(iris.feature_names[y_index])
Excercise: Can you choose x_index and y_index to find a plot where it is easier to seperate the different classes of irises.