  Variable selection is of significant importance for classification and
regression tasks in machine learning and statistical applications where both
predictability and explainability are needed. In this paper, a Copula Entropy
(CE) based method for variable selection which use CE based ranks to select
variables is proposed. The method is both model-free and tuning-free.
Comparison experiments between the proposed method and traditional variable
selection methods, such as Distance Correlation, Hilbert-Schmidt Independence
Criterion, Stepwise Selection, regularized generalized linear models and
Adaptive LASSO, were conducted on the UCI heart disease data. Experimental
results show that CE based method can select the `right' variables out more
effectively and derive better interpretable results than traditional methods do
without sacrificing accuracy performance. It is believed that CE based variable
selection can help to build more explainable models.
