What are some advanced feature selection techniques in machine learning?

What are some advanced feature selection techniques in machine learning?

Advanced feature selection techniques include methods like Recursive Feature Elimination (RFE), Lasso Regression, and Tree-based methods.

Can you explain how Recursive Feature Elimination works?

Sure! RFE works by recursively removing the least important features based on a model's performance until the desired number of features is reached.

What is Lasso Regression and how does it help in feature selection?

Lasso Regression adds a penalty equal to the absolute value of the magnitude of coefficients, effectively shrinking some coefficients to zero, thus performing feature selection.

Are there any specific scenarios where these techniques are particularly useful?

Yes, these techniques are especially useful in high-dimensional datasets where the number of features exceeds the number of observations, helping to reduce overfitting.