How does feature selection affect overfitting in machine learning models?

How does feature selection affect overfitting in machine learning models?

Feature selection helps reduce overfitting by eliminating irrelevant or redundant features, leading to simpler models.

Can you explain why simpler models are less prone to overfitting?

Simpler models have fewer parameters, which reduces the risk of capturing noise in the training data, thus generalizing better.

What techniques can I use for feature selection?

Common techniques include filter methods, wrapper methods, and embedded methods, each with its own advantages.

Are there any downsides to feature selection?

Yes, if important features are removed, it can lead to underfitting. It's crucial to balance feature selection carefully.