70. Hyperparameter Tuning: Finding the Best Settings.

Short summary

Hyperparameter tuning replaces manual guessing with systematic optimization to find the best model settings. Grid search exhaustively tests all combinations but scales poorly with many parameters; random search samples wider ranges efficiently with fewer trials. Use random search first to identify important parameters, then refine with grid search or Bayesian optimization tools like Optuna.

• •Grid search exhaustively tests all parameter combinations but scales poorly
• •Random search samples from wider ranges more efficiently with fewer trials
• •Use random search to identify important parameters, then refine with Bayesian optimization

Generated with AI, which can make mistakes.