Hands-On Machine Learning for Cybersecurity

Investigating precision-recall curve and area

The following the code for investigating precision-recall curve:

from itertools import cyclelr = LogisticRegression(C = 0.01, penalty = 'l1')lr.fit(X_train_undersample,y_train_undersample.values.ravel())y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal', 'red', 'yellow', 'green', 'blue','black'])plt.figure(figsize=(5,5))j = 1for i,color in zip(thresholds,colors): y_test_predictions_prob = y_pred_undersample_proba[:,1] > i  precision, recall, thresholds = precision_recall_curve(y_test_undersample,y_test_predictions_prob)  # Plot Precision-Recall curve ...