variable importances

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.

Practitioners refer to variable importances when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.