  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.
