Support Vector Machines

This module contains a single function used to help visualize the decision rule of an SVM.

import numpy as np
from sklearn.svm import SVC
from ISLP.svm import plot

Make a toy dataset

rng = np.random.default_rng(1)
X = rng.normal(size=(100, 5))
X[:40][:,3:5] += 2
Y = np.zeros(X.shape[0])
Y[:40] = 1

Fit an SVM classifier

svm = SVC(kernel='linear')
svm.fit(X, Y)

SVC(kernel='linear')

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plot(X, Y, svm)

Slicing through different features

When we generated our data, the real differences ware in the 4th and 5th coordinates. We can see this by taking a cross-section through the data that includes this coordinate as one of the axes in the plot.

plot(X, Y, svm, features=(3, 4))