Glossary term
Glossary term
Foundations
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.
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.
Created for this library
A risk modeling team reports variable importances from its boosted-tree scorecard to communicate the drivers to business reviewers.
A retail forecasting team reviews variable importances after retraining to confirm key business drivers remain influential.
A churn team reports variable importances each release so the retention team can adjust playbooks to the latest drivers.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License