2A.ML101.0: What is machine learning?¶
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.
Source: Course on machine learning with scikit-learn by Gaël Varoquaux
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:
# Start pylab inline mode, so figures will appear in the notebook %matplotlib inline
# Import the example plot from the figures directory from plot_sgd_separator import plot_sgd_separator plot_sgd_separator()
c:python370_x64libsite-packagessklearnlinear_modelstochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead. DeprecationWarning)
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 plot_linear_regression 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.
Data in scikit-learn¶
Most machine learning algorithms implemented in scikit-learn expect data
to be stored in a two-dimensional array or matrix. The arrays can be
numpy arrays, or in some cases
scipy.sparse matrices. The
size of the array is expected to be
n_samples: The number of samples: each sample is an item to process (e.g. classify). A sample can be a document, a picture, a sound, a video, an astronomical object, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.
n_features: The number of features or distinct traits that can be used to describe each item in a quantitative manner. Features are generally real-valued, but may be boolean or discrete-valued in some cases.
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
can be useful, in that they are much more memory-efficient than numpy
A Simple Example: the Iris Dataset¶
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='iris_setosa.jpg')) print("Iris Setosa\n") display(Image(filename='iris_versicolor.jpg')) print("Iris Versicolor\n") display(Image(filename='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
What might the
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
Loading the Iris Data with Scikit-learn¶
Scikit-learn has a very straightforward set of data on these iris species. The data consist of the following:
Features in the Iris dataset:
sepal length in cm
sepal width in cm
petal length in cm
petal width in cm
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
of the dataset:
n_samples, n_features = iris.data.shape print(n_samples) print(n_features) print(iris.data)
150 4 [5.1 3.5 1.4 0.2]
The information about the class of each sample is stored in the
target attribute of the dataset:
(150, 4) (150,)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
The names of the classes are stored in the last attribute, namely
['setosa' 'versicolor' 'virginica']
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.