What is feature importance in machine learning models?

What is feature importance in machine learning models?

Feature importance refers to techniques that assign a score to input features based on how useful they are at predicting the target variable.

How can I calculate feature importance?

You can calculate feature importance using methods like permutation importance, tree-based feature importance, or SHAP values.

Why is feature importance important?

Understanding feature importance helps in model interpretation, feature selection, and improving model performance.

Can feature importance change with different models?

Yes, feature importance can vary between different models, as each model may interpret the data differently.