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.…