Fast Prediction of New Feature Utility

Understand

We study the new feature utility prediction problem: statistically testing whether adding a new feature to the data representation can improve predictive accuracy on a supervised learning task.

• In many applications, identifying new informative features is the primary pathway for improving performance.
• However, evaluating every potential feature by re-training the predictor with it can be costly.
• The paper describes an efficient, learner-independent technique for estimating new feature utility without re-training based on the current predictor's outputs.