Machine Learning for OpenCV 4 - Second Edition

Evaluating the perceptron classifier

In the following steps, you will be evaluating the trained perceptron on the test data:

1. In order to find out how good our perceptron performs, we can calculate the accuracy score on all data samples:

In [10]: from sklearn.metrics import accuracy_score...      accuracy_score(p.predict(X), y)Out[10]: 1.0

Perfect score!

2. Let's have a look at the decision landscape by bringing back our plot_decision_boundary from the earlier chapters:

In [10]: def plot_decision_boundary(classifier, X_test, y_test):...          # create a mesh to plot in...          h = 0.02 # step size in mesh...          x_min, x_max = X_test[:, 0].min() - 1, X_test[:, 0].max() + 1...          y_min, y_max = X_test[:, 1].min() - 1, X_test[:, 1].max() + 1... xx, yy = np.meshgrid(np.arange(x_min, ...