hyperparameter-tuning

Tune machine learning hyperparameters using Bayesian optimization by defining a search space and an objective metric to systematically explore parameter configurations and maximize model performance within a compute budget.

Cross-validation evaluates hyperparameter search strategies by applying configurations across cross-validated folds, ensuring reliable performance estimates when using grid, random, Bayesian optimization, or Hyperband approaches.

You need libraries such as Optuna or Ray Tune to automate hyperparameter tuning, as they provide the required mechanisms for search spaces, pruning, logging, and result analysis.

Define search spaces for conditional hyperparameters by specifying types like categorical, integer, and float with ranges, allowing systematic exploration of parameter configurations based on parent parameter values.

Hyperband pruning improves hyperparameter tuning efficiency by terminating poorly performing configurations early during cross-validated evaluations, maximizing model performance within a limited compute budget.

Analyze hyperparameter tuning results to identify influential parameters and retrain the final model on the full training data using the best configuration found during the systematic exploration.