High Dimensional Model Representation as a Glass Box in Supervised Machine Learning
This provides a method for better model interpretability in machine learning, though it appears incremental as it applies an existing representation technique to new contexts.
The paper tackles the problem of interpreting supervised machine learning models by applying High Dimensional Model Representation (HDMR) as a glass box for explanation, showing its utility in applications such as analyzing information leakage and estimating HDMR from black box models.
Prediction and explanation are key objects in supervised machine learning, where predictive models are known as black boxes and explanatory models are known as glass boxes. Explanation provides the necessary and sufficient information to interpret the model output in terms of the model input. It includes assessments of model output dependence on important input variables and measures of input variable importance to model output. High dimensional model representation (HDMR), also known as the generalized functional ANOVA expansion, provides useful insight into the input-output behavior of supervised machine learning models. This article gives applications of HDMR in supervised machine learning. The first application is characterizing information leakage in ``big-data'' settings. The second application is reduced-order representation of elementary symmetric polynomials. The third application is analysis of variance with correlated variables. The last application is estimation of HDMR from kernel machine and decision tree black box representations. These results suggest HDMR to have broad utility within machine learning as a glass box representation.