scikit-learn Cookbook , Second Edition - Second Edition

Making a scorer

To make a scorer, you need:

• A scoring function that compares y_test, the ground truth, with y_pred, the predictions
• To determine whether a high score is good or bad

Before passing the SVR regressor to the cross-validation, make a scorer by supplying two elements:

8. In practice, begin by importing the make_scorer function:

from sklearn.metrics import make_scorer

9. Use this sample scoring function:

#Only works for this iris example with targets 0 and 1def for_scorer(y_test, orig_y_pred):    y_pred = np.rint(orig_y_pred).astype(np.int)   #rounds prediction to the nearest integer    return accuracy_score(y_test, y_pred)

The np.rint function rounds off the prediction to the nearest integer, hopefully one of the targets, 0 or 1. The ...