Logistic Regression Evaluation

Accuracy alone rarely tells the full story for a classification model - properly evaluating Logistic Regression means looking at several complementary metrics.

The Confusion Matrix

                Predicted 0   Predicted 1
Actual 0           TN              FP
Actual 1           FN              TP

# TP = True Positive, TN = True Negative
# FP = False Positive, FN = False Negative

Key Metrics

Accuracy  = (TP + TN) / (TP + TN + FP + FN)
Precision = TP / (TP + FP)
Recall    = TP / (TP + FN)
F1 Score  = 2 * (Precision * Recall) / (Precision + Recall)

Implementing in Python

from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score

y_pred = model.predict(X_test)
y_proba = model.predict_proba(X_test)[:, 1]

print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
print("ROC-AUC:", roc_auc_score(y_test, y_proba))

Choosing the Right Metric

Accuracy can be misleading on imbalanced data - for example, in fraud detection, precision and recall matter far more than overall accuracy, since missing a fraud case (false negative) is usually costlier than a false alarm.

Coming Up Next

Next, you'll see where Logistic Regression is actually applied across different industries.