Better Model Selection with a new Definition of Feature Importance
This work addresses model selection for practitioners in machine learning, offering an incremental improvement over existing methods.
The paper tackles the problem of model selection by introducing a new feature importance definition based on the Coefficient of Variation of feature weights to capture dispersion over samples, resulting in improved time efficiency and accuracy compared to general cross-validation methods.
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.