Home▸AI & Machine Learning▸Random Search vs Bayesian Optimisation: Tuning XGBoost on a Bank Marketing Dataset

🎯 Random Search vs Bayesian Optimisation: Tuning XGBoost

Watch two 3D hyperparameter-search landscapes fill with trial points side by side: uniform Random Search versus Bayesian TPE, converging on the best validation score for a gradient-boosted model.

AI & Machine Learning3DAdvanced60 FPS

Two identical hyperparameter landscapes sit side by side, one searched by uniform Random Search, the other by a simplified Tree-structured Parzen Estimator (Bayesian TPE) — watch which strategy climbs to the peak validation score faster.

🔬 What It Demonstrates

Random Search samples blindly every trial. Bayesian TPE models which regions of past trials scored well versus poorly, then samples new candidates that maximise the good/bad density ratio — biasing later trials toward the peak.

🎮 How to Use

Set a trial budget and TPE exploitation strength, then press Autoplay (or Step) to run both strategies trial-by-trial. Compare the best AUC found by each and watch how quickly TPE's blue points cluster near the summit.

💡 Did You Know?

Hyperopt's TPE sampler underlies many production AutoML pipelines precisely because it usually needs far fewer trials than grid or random search to reach a near-optimal gradient-boosted model configuration.

⚙ Under the hood

Watch two 3D hyperparameter-search landscapes fill with trial points side by side: uniform Random Search versus Bayesian TPE, converging on the best validation score for a gradient-boosted model.

Machine LearningHyperparameter TuningXGBoostBayesian OptimizationGradient BoostingData ScienceThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install