What is the difference between hyperparameter tuning and feature selection?

What is the difference between hyperparameter tuning and feature selection?

Hyperparameter tuning involves optimizing the parameters of a model to improve its performance, while feature selection is about choosing the most relevant features for the model.

Can you give me an example of hyperparameter tuning?

Sure! An example of hyperparameter tuning is adjusting the learning rate in a neural network to find the best value that minimizes the loss function.

And what about feature selection? How is it done?

Feature selection can be done using methods like recursive feature elimination, where you iteratively remove the least important features based on model performance.

Which one is more important for model performance?

Both are important! Hyperparameter tuning can enhance model accuracy, while feature selection can reduce overfitting and improve interpretability.