Valutazione delle prestazioni

Rilevamento delle frodi in Python

Charlotte Werger

Data Scientist

L’accuracy non basta

Dimentica l’accuracy quando lavori su problemi di frodi

Falsi positivi, falsi negativi e frodi reali rilevate

Compromesso precision–recall

Ottenere metriche di performance

# Import the packages
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score

# Calculate average precision and the PR curve
average_precision = average_precision_score(y_test, predicted)

# Obtain precision and recall
precision, recall, _ = precision_recall_curve(y_test, predicted)

Curva precision–recall

Curva ROC per confrontare algoritmi

# Obtain model probabilities
probs = model.predict_proba(X_test)

# Print ROC_AUC score using probabilities 
print(metrics.roc_auc_score(y_test, probs[:, 1]))

0.9338879319822626

from sklearn.metrics import classification_report, confusion_matrix

# Obtain predictions 
predicted = model.predict(X_test)

# Print classification report using predictions
print(classification_report(y_test, predicted))

  precision    recall  f1-score   support

        0.0       0.99      1.00      1.00      2099
        1.0       0.96      0.80      0.87        91

avg / total       0.99      0.99      0.99      2190

# Print confusion matrix using predictions
print(confusion_matrix(y_test, predicted))

[[2096    3]
 [  18   73]]

Passons à la pratique !

Rilevamento delle frodi in Python