Variable importances are scores that estimate how much each feature contributes to a model’s predictions or decisions. Different importance methods can reveal different aspects of model behavior and support interpretation, debugging, and feature selection.
A set of scores that indicates the relative importance of each feature to the model. For example, consider a decision tree that estimates house prices. Suppose this decision tree uses three features: size, age, and style. If a set of variable importances for the three features are calculated to be \size=5.8, age=2.5, style=4.7\, then size is more important to the decision tree than age or style. Different variable importance metrics exist, which can inform ML experts about different aspects of models.