What are some common mistakes in hyperparameter tuning?

What are some common mistakes in hyperparameter tuning?

Some common mistakes include not using enough data, overfitting, and not properly validating the model.

Can you explain how overfitting happens during hyperparameter tuning?

Overfitting occurs when the model learns noise in the training data instead of the actual patterns, often due to overly complex models or too many hyperparameter adjustments.

What strategies can I use to avoid these mistakes?

To avoid mistakes, use cross-validation, start with a simple model, and gradually increase complexity while monitoring performance.

How important is the choice of hyperparameters?

The choice of hyperparameters is crucial as it can significantly impact model performance and generalization to unseen data.