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Hands-On Predictive Analytics with Python
book

Hands-On Predictive Analytics with Python

by Alvaro Fuentes
December 2018
Beginner to intermediate content levelBeginner to intermediate
330 pages
8h 32m
English
Packt Publishing
Content preview from Hands-On Predictive Analytics with Python

Receiver Operating Characteristic (ROC) and precision-recall curves

As we mentioned before, to obtain the final predictions from the predicted probabilities we get from the model, we need a classification threshold. Therefore, if we change the threshold we will get a different classifier, a different confusion matrix, and, of course, different classification metrics. Let's see what happens if we use a threshold of 0.4:

threshold = 0.4y_pred_prob = rf.predict_proba(X_test)[:,1]y_pred = (y_pred_prob > threshold).astype(int)precision = precision_score(y_test, y_pred)recall = recall_score(y_test, y_pred)print("Precision: {:0.1f}%, Recall: {:.1f}%".format(100*precision, 100*recall))CM(y_test, y_pred)

We get the following results:

For making the ...

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Publisher Resources

ISBN: 9781789138719Supplemental Content