Hyperparameter Tuning (Grid / Random Search)

Summary

Systematically searching for the best model settings — grid search tries every combination in a defined range, random search samples combinations randomly, often nearly as effective and much faster.

How to Learn This

1Run a grid search over 2-3 hyperparameters for a model you've trained.
2Compare grid search results to a random search over the same range.
3Learn why tuning must always be validated on held-out data, never the training set.

Regression Metrics (MAE, RMSE, R²)

Regularization (L1 / L2)

More topics in Model Evaluation & Tuning

Classification Metrics (Accuracy, Precision, Recall, F1)Confusion Matrix ROC-AUC Curve Regression Metrics (MAE, RMSE, R²)Regularization (L1 / L2)Ensemble Methods Model Selection Strategy

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