Hyperparameter Tuning (Grid/Random Search)

Summary

Systematically searching combinations of model hyperparameters to find the configuration that performs best — grid search is exhaustive, random search is often more efficient.

How to Learn This

1Run GridSearchCV and RandomizedSearchCV on the same model and compare runtime vs. results.
2Learn to define a sensible hyperparameter search space, not just wide random guesses.
3Always tune using cross-validation, never the final test set.

Handling Imbalanced Data

Ensemble Methods (Bagging, Boosting)

More topics in Feature Engineering & Model Evaluation

Feature Scaling & Normalization One-Hot & Label Encoding Feature Selection Handling Imbalanced Data Ensemble Methods (Bagging, Boosting)XGBoost & LightGBM Evaluation Metrics (Accuracy, Precision, Recall, F1)ROC-AUC Confusion Matrix Bias-Variance Tradeoff Overfitting & Regularization (L1/L2)

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