📏 05 — Classification Metrics¶

Accuracy is not enough, especially with imbalanced data.

Confusion Matrix¶

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred)
print(cm)

Term	Meaning
TP	predicted positive, actually positive
TN	predicted negative, actually negative
FP	predicted positive, actually negative
FN	predicted negative, actually positive

Accuracy¶

from sklearn.metrics import accuracy_score

accuracy_score(y_test, y_pred)

Good when classes are balanced and errors have similar cost.

Precision and Recall¶

from sklearn.metrics import precision_score, recall_score, f1_score

precision_score(y_test, y_pred)
recall_score(y_test, y_pred)
f1_score(y_test, y_pred)

Metric	Focus
Precision	avoid false positives
Recall	avoid false negatives
F1	balance precision and recall

ROC-AUC¶

from sklearn.metrics import roc_auc_score

roc_auc_score(y_test, y_proba[:, 1])

ROC-AUC measures ranking quality across thresholds.

Metric Choice¶

Scenario	Metric Priority
fraud detection	recall + precision
spam filter	precision
medical screening	recall
balanced simple task	accuracy

Next¶

➡️ 06-exercises