What are the best practices for variable selection in machine learning?

What are the best practices for variable selection in machine learning?

Some best practices include understanding the domain, using correlation analysis, and applying regularization techniques.

Can you explain how correlation analysis helps in variable selection?

Correlation analysis helps identify relationships between variables, allowing you to select those that contribute most to the model.

What is regularization and how does it aid in variable selection?

Regularization techniques like Lasso and Ridge penalize less important variables, effectively reducing their impact on the model.

Are there any tools or libraries you recommend for variable selection?

Yes, libraries like Scikit-learn in Python offer built-in functions for feature selection, including recursive feature elimination.