svm

Module: svm

Helper functions for SVMs

This module contains functions used for the SVM lab of ISLP. Currently it contains just a simple function to plot decision boundary and points through a two-dimensional slice for an SVM classifier.

ISLP.svm.plot(X, Y, svm, features=(0, 1), xlim=None, nx=300, ylim=None, ny=300, ax=None, decision_cmap=<matplotlib.colors.ListedColormap object>, scatter_cmap=<matplotlib.colors.ListedColormap object>, alpha=0.2)

Graphical representation of fitted support vector classifier.

There are two types of support vectors:

• Points violating the margin but correctly classified. These are marked with a black ‘+’.

• Misclassified points. These are marked with a red ‘x’.

Parameters:

Xarray-like of shape (n_samples, n_features)

Features used in fitting svm. Assumed to have at least 2 columns.

Yarray-like of shape (n_samples,)

Labels used in fitting svm. Used to color points by class.

svmsklearn.svm.SVC

Fitted support vector classifier. Assumed that svm has been fit on (X,Y).

features(int, int), default=(0, 1)

Which columns of X used for plotting. If more then two features, remaining features are set at mean values for 2-dimensional slice.

xlim(float, float), optional

Range of values for x-axis of plot.

nxint, default=300

Resolution of grid for x-axis.

ylim(float, float), optional

Range of values for y-axis of plot.

nyint, default=300

Resolution of grid for y-axis.

axa matplotlib axis

decision_cmapa matplotlib colormap for coloring decision rule.

scatter_cmapa matplotlib colormap for coloring points by class.

alphafloat

Alpha level for opacity of decision rule.