Machine Learning for OpenCV 4 - Second Edition

Applying the perceptron to data that is not linearly separable

In the following steps, you will learn to build a perceptron to separate a nonlinear data:

1. Since the perceptron is a linear classifier, you can imagine that it would have trouble trying to classify data that is not linearly separable. We can test this by increasing the spread (cluster_std) of the two blobs in our toy dataset so that the two blobs start overlapping:

In [12]: X, y = make_blobs(n_samples=100, centers=2,...      cluster_std=5.2, random_state=42)...      y = 2 * y - 1

2. We can plot the dataset again using matplotlib's scatter function:

In [13]: plt.scatter(X[:, 0], X[:, 1], s=100, c=y);...      plt.xlabel('x1')...      plt.ylabel('x2')

As is evident in the following screenshot, this ...