What are the best feature selection techniques?

What are the best feature selection techniques I should consider for my machine learning model?

There are several effective feature selection techniques, including Recursive Feature Elimination (RFE), Lasso Regression, and Tree-based methods like Random Forest. Each has its strengths depending on your dataset.

Can you explain how Recursive Feature Elimination works?

Sure! RFE works by recursively removing the least important features based on the model's performance until the desired number of features is reached. It helps in identifying the most significant features.

What about Lasso Regression? How does it help in feature selection?

Lasso Regression adds a penalty equal to the absolute value of the magnitude of coefficients. This can shrink some coefficients to zero, effectively performing feature selection by eliminating less important features.

Are there any specific scenarios where one technique is better than the others?

Yes, for high-dimensional datasets, Lasso is often preferred due to its ability to reduce dimensionality. RFE is great when you want a more thorough selection process, while tree-based methods are robust to feature interactions.