You picked a model. You trained it. You got decent accuracy. Then someone asks: did you tune the hyperparameters? You picked max_depth=5 because it felt right. Learning rate 0.1 because you saw it in a tutorial. Number of trees because 100 is a round number. That's guessing. Hyperparameter tuning replaces guessing with a systematic search. It finds the combination of settings that actually works best for your specific data. What You'll Learn Here What hyperparameters are and why they matter Grid search: exhaustive but slow Random search: faster and often just as good Bayesian optimization with Optuna: smarter search How to avoid overfitting your validation set during tuning Nested cross-validation for honest evaluation Practical tuning strategy for real projects Parameters vs Hyperparameters First the distinction, because people mix these up. Parameters are learned by the model during training. The weights in a neural network. The split thresholds in a decision tree. You don't set these.…