How do I tune hyperparameters in machine learning models?

How do I tune hyperparameters in machine learning models?

Tuning hyperparameters involves adjusting the settings of your model to improve performance. Common methods include grid search, random search, and Bayesian optimization.

What is grid search and how does it work?

Grid search is a method where you define a grid of hyperparameter values and evaluate the model's performance for each combination. It helps find the best set of hyperparameters.

Are there any libraries in Python that can help with hyperparameter tuning?

Yes, libraries like Scikit-learn, Optuna, and Hyperopt provide tools for hyperparameter tuning, making the process easier and more efficient.

How do I implement random search in Python?

You can use Scikit-learn's RandomizedSearchCV to implement random search. It allows you to specify the parameter distribution and the number of iterations.