How do hyperparameters affect overfitting in machine learning models?

How do hyperparameters affect overfitting in machine learning models?

Hyperparameters are crucial as they control the learning process. If set incorrectly, they can lead to overfitting.

Can you give me an example of a hyperparameter that might cause overfitting?

Sure! The depth of a decision tree is a hyperparameter. A very deep tree can model noise in the training data, leading to overfitting.

What are some strategies to prevent overfitting related to hyperparameters?

You can use techniques like cross-validation, regularization, and pruning to help mitigate overfitting.

How do I choose the right hyperparameters for my model?

You can use grid search or random search methods to explore different combinations of hyperparameters and find the best fit.