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• W2945490211 abstract "We develop a simple and computationally efficient significance test for the features of a machine learning model. Our forward-selection approach applies to any model specification, learning task and variable type. The test is non-asymptotic, straightforward to implement, and does not require model refitting. It identifies the statistically significant features as well as feature interactions of any order in a hierarchical manner, and generates a model-free notion of feature importance. Experimental and empirical results illustrate its performance." @default.
• W2945490211 created "2019-05-29" @default.
• W2945490211 creator A5042142175 @default.
• W2945490211 creator A5047704387 @default.
• W2945490211 date "2019-05-23" @default.
• W2945490211 modified "2023-09-27" @default.
• W2945490211 title "Computationally Efficient Feature Significance and Importance for Machine Learning Models" @default.
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• W2945490211 hasPublicationYear "2019" @default.
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