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Hyperparameter search done right: GridSearchCV, RandomizedSearchCV, and Optuna with CV built in
Hyperparameter tuning is easy to get started with and surprisingly easy to get wrong.
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Cross-validation done right: k-fold, stratification, and time-series traps
A machine learning model can achieve 97% cross-validation accuracy and still fail spectacularly when deployed. The culprit is often not the algorithm, hyperparameters, or training data. It’s the way t
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Distributed hyperparameter tuning: CrossValidator, ParamGridBuilder, and TrainValidationSplit
Hyperparameter tuning is often introduced as a modeling task: try different settings, compare metrics, and select the winner. At scale, it becomes a distributed systems problem.