What is the difference between feature selection and feature engineering?

What is the difference between feature selection and feature engineering?

Feature selection involves choosing a subset of relevant features for model training, while feature engineering is about creating new features from existing data.

Can you give me examples of feature selection techniques?

Sure! Common techniques include Recursive Feature Elimination (RFE), Lasso Regression, and tree-based methods like Random Forest.

What are some popular feature engineering methods?

Popular methods include one-hot encoding, normalization, polynomial features, and creating interaction terms.

When should I use feature selection over feature engineering?

Use feature selection when you have too many features that may lead to overfitting. Feature engineering is useful when you need to enhance the predictive power of your model.