Showing papers on "High-dimensional model representation published in 2022"
TL;DR: RS-HDMR-GPR (Random Sampling High-Dimensional Model Representation Gaussian Process Regression) as discussed by the authors is a Python implementation of RS-HDRM.
7 citations
TL;DR: Zhang et al. as discussed by the authors proposed a method based on high dimensional model representation (HDMR) to reveal the structure of prediction models by detecting the feature interactions that are embedded in the models.
Abstract: In recent years, machine learning models have been employed for prediction in various domains. While the prediction performance has obviously improved, some models have become too complex to understand, and these models are called black-box models. Detecting the feature interactions is a useful technique to gain insight into the structure of black-box models. In this paper, we propose a method based on high dimensional model representation (HDMR) to reveal the structure of prediction models by detecting the feature interactions that are embedded in the models. The HDMR-based method can detect the k -way interactions without any constraints on k and can detect the interactions from both classification and regression models. Moreover, this method is model-agnostic and can detect both global and local interactions. Experiments on some synthetic and real datasets demonstrate that the HDMR-based method can detect feature interactions effectively and improve the model interpretability.
3 citations