Machine Learning for OpenCV 4 - Second Edition

Visualizing conditional probabilities

By referring to the following steps, you will be able to visualize conditional probabilities:

1. For this, we will slightly modify the plot function from the previous example. We start out by creating a mesh grid between (x_min, x_max) and (y_min, y_max):

In [18]: def plot_proba(model, 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, x_max, h),...                               np.arange(y_min, y_max, h))

2. Then, we flatten xx and yy and add them column-wise to the feature matrix, X_hypo:

... X_hypo = np.column_stack((xx.ravel().astype(np.float32), ...