Lesson 6 of 8Article19 min
Model Evaluation and Validation
Model Evaluation and Validation
Evaluation metrics by problem type
- Regression: MAE, RMSE, R2.
- Classification: accuracy, precision, recall, F1, ROC-AUC.
- Imbalanced classes require precision-recall focus.
Cross-validation example
K-fold score estimate
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=200, random_state=42)
scores = cross_val_score(model, X, y, cv=5, scoring="f1")
print("Fold scores:", scores)
print("Mean F1:", scores.mean())