Marginal Effect of Hyperparameter Tuning with XGBoost

Efficiently tuning XGBoost hyperparameters often requires moving beyond brute-force methods like grid search. The `hyperopt` library offers a powerful alternative using a Bayesian approach known as the Tree-structured Parzen Estimator (TPE). This algorithm intelligently guides its search by building separate probability models for "good" and "bad" hyperparameter combinations based on past results. It then strategically samples from the "good" model to find new combinations that maximize the expected performance improvement. This analysis also explores the crucial trade-off between expanding the hyperparameter search space for marginal gains versus the significant increase in computation time.