Hyperparameter Search and Bayesian Optimization Explained With Numbers

Stop guessing learning rates. Learn how to tune models with a plan: random search, nested cross-validation, Hyperband, and Bayesian optimization. In lesson 45 of Machine Learning from Zero to Advanced, we separate parameters from hyperparameters, explain why log scales matter, and show why random search beats grid search using the effective-dimension argument. We then see how nested cross-validation removes optimistic bias, how successive halving and Hyperband save compute, and how a surrogate model with expected improvement chooses the next trial. Expected improvement is worked out by hand with real numbers. We finish by building random search and a tiny one-dimensional Bayesian optimizer in Python, using Optuna on a real model, and covering budgeting, reproducibility, and validation overfitting. Next up: ARIMA and exponential smoothing. 0:00 Intro 0:43 Parameters, Hyperparameters and Search Spaces 1:58 Grid Search vs Random Search 3:33 Nested Cross-Validation 4:52 Successive Halving and Hyperband 6:04 Bayesian Optimization 9:43 Python: Random Search and a 1D Bayesian Optimizer 10:57 Optuna on a Real Model 12:17 Budgeting, Reproducibility and Validation Overfitting 13:29 Recap and Next Lesson #MachineLearning #HyperparameterTuning #BayesianOptimization #Optuna #DataScience