Classification metrics beyond accuracy for imbalanced problems

                  from sklearn.metrics import (
    average_precision_score,
    classification_report,
    precision_recall_curve,
    roc_auc_score,
)

probabilities = model.predict_proba(X_valid)[:, 1]
predictions = (probabilities >= 0.35).astype(int)

print('ROC AUC:', roc_auc_score(y_valid, probabilities))
print('PR AUC:', average_precision_score(y_valid, probabilities))
print(classification_report(y_valid, predictions, digits=3))

precision, recall, thresholds = precision_recall_curve(y_valid, probabilities)
print('threshold samples:', list(zip(thresholds[:5], precision[:5], recall[:5])))