What are the applications of variable selection in machine learning?

What are the applications of variable selection in machine learning?

Variable selection is crucial in machine learning for improving model performance, reducing overfitting, and enhancing interpretability.

Can you give me some specific examples of where it's used?

Sure! It's used in fields like genomics for selecting relevant genes, in finance for risk assessment, and in marketing for customer segmentation.

How does variable selection improve model performance?

By eliminating irrelevant features, variable selection helps models focus on the most informative data, leading to better predictions.

Are there any techniques for variable selection?

Yes, common techniques include LASSO, recursive feature elimination, and tree-based methods like Random Forest.