What are the best practices for tuning machine learning models in Python?

What are the best practices for tuning machine learning models in Python?

Some best practices include using cross-validation, grid search, and random search for hyperparameter tuning.

Can you explain how cross-validation works?

Cross-validation involves splitting your dataset into training and testing sets multiple times to ensure your model's performance is consistent.

What is grid search and how do I implement it?

Grid search is a method to systematically work through multiple combinations of hyperparameters. You can implement it using GridSearchCV from scikit-learn.

Are there any libraries that can help with this?

Yes, libraries like scikit-learn, Optuna, and Hyperopt are great for hyperparameter tuning in Python.