A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping

The tutorial shows how to use Hyperopt and TPE to tune a conditional scikit-learn pipeline.

It compares Logistic Regression and SVM on the Breast Cancer dataset with cross-validation to find strong hyperparameters.

Early stopping and the Trials object help make the Bayesian optimization loop more efficient and reproducible.

The approach provides a practical framework for model selection across multiple families and can extend to more complex ML workflows.