Regression Metrics (MAE, RMSE, R²)

Summary

MAE (mean absolute error) is intuitive and robust to outliers, RMSE penalizes large errors more heavily, and R² describes how much variance the model explains — each tells a different part of the story.

How to Learn This

1Calculate all 3 metrics for a regression model's predictions.
2Learn why RMSE is more sensitive to large errors than MAE.
3Practice explaining R² in plain language ('the model explains X% of the variation').

ROC-AUC Curve

Hyperparameter Tuning (Grid / Random Search)

More topics in Model Evaluation & Tuning

Classification Metrics (Accuracy, Precision, Recall, F1)Confusion Matrix ROC-AUC Curve Hyperparameter Tuning (Grid / Random Search)Regularization (L1 / L2)Ensemble Methods Model Selection Strategy

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