Gaussian Function On Response Surface Estimation

TL;DR

This paper introduces a Gaussian process-based metamodeling framework for interpreting complex machine learning models by analyzing input-output relationships through response surface estimation, without pre-assumed models.

Contribution

It presents a novel Gaussian process approach that combines interpolation and trend modeling for effective interpretation of black-box models.

Findings

Effective response surface estimation demonstrated

Quantitative assessment shows interpretability benefits

No pre-assumed models required

Abstract

We propose a new framework for 2-D interpreting (features and samples) black-box machine learning models via a metamodeling technique, by which we study the output and input relationships of the underlying machine learning model. The metamodel can be estimated from data generated via a trained complex model by running the computer experiment on samples of data in the region of interest. We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model. The optimization procedure for the variable importance parameter theta is to maximize the likelihood function. This theta corresponds to the correlation of individual variables with the target response. There is no need for any pre-assumed models since it depends on empirical observations. Experiments demonstrate the potential of the interpretable model through quantitative assessment of the predicted samples.