What are the common hyperparameters for different machine learning models?

What are the common hyperparameters for different machine learning models?

Common hyperparameters include learning rate, batch size, and number of epochs. Each model may have specific ones too.

Can you give examples for specific models like SVM and neural networks?

Sure! For SVM, common hyperparameters are C (regularization) and kernel type. For neural networks, you often adjust layers, activation functions, and dropout rates.

How do these hyperparameters affect model performance?

Hyperparameters can significantly impact performance; for instance, a high learning rate might lead to overshooting the optimal solution.

Are there any tools to help with hyperparameter tuning?

Yes, tools like Grid Search, Random Search, and Bayesian Optimization can help automate hyperparameter tuning.