Better Model Selection with a new Definition of Feature Importance

• URL: http://arxiv.org/abs/2009.07708v1
• Date: Wed, 16 Sep 2020 14:32:22 GMT
• Title: Better Model Selection with a new Definition of Feature Importance
• Authors: Fan Fang, Carmine Ventre, Lingbo Li, Leslie Kanthan, Fan Wu, Michail Basios
• Abstract summary: Feature importance aims at measuring how crucial each input feature is for model prediction. In this paper, we propose a new tree-model explanation approach for model selection.
• Score: 8.914907178577476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature importance aims at measuring how crucial each input feature is for model prediction. It is widely used in feature engineering, model selection and explainable artificial intelligence (XAI). In this paper, we propose a new tree-model explanation approach for model selection. Our novel concept leverages the Coefficient of Variation of a feature weight (measured in terms of the contribution of the feature to the prediction) to capture the dispersion of importance over samples. Extensive experimental results show that our novel feature explanation performs better than general cross validation method in model selection both in terms of time efficiency and accuracy performance.

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