Gaussian Process Regression with Local Explanation

07/03/2020
by   Yuya Yoshikawa, et al.
0

Gaussian process regression (GPR) is a fundamental model used in machine learning. Owing to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications. However, in GPR, how the features of an input contribute to its prediction cannot be interpreted. Herein, we propose GPR with local explanation, which reveals the feature contributions to the prediction of each sample, while maintaining the predictive performance of GPR. In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model. The weight vector of the locally linear model is assumed to be generated from multivariate Gaussian process priors. The hyperparameters of the proposed models are estimated by maximizing the marginal likelihood. For a new test sample, the proposed model can predict the values of its target variable and weight vector, as well as their uncertainties, in a closed form. Experimental results on various benchmark datasets verify that the proposed model can achieve predictive performance comparable to those of GPR and superior to that of existing interpretable models, and can achieve higher interpretability than them, both quantitatively and qualitatively.

READ FULL TEXT
research
05/07/2020

A Locally Adaptive Interpretable Regression

Machine learning models with both good predictability and high interpret...
research
03/13/2017

Multivariate Gaussian and Student-t Process Regression for Multi-output Prediction

Gaussian process for vector-valued function model has been shown to be a...
research
03/13/2020

Neural Generators of Sparse Local Linear Models for Achieving both Accuracy and Interpretability

For reliability, it is important that the predictions made by machine le...
research
12/14/2021

Local Prediction Pools

We propose local prediction pools as a method for combining the predicti...
research
10/20/2022

Scalable Bayesian Transformed Gaussian Processes

The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem...
research
10/12/2019

Adaptive design for Gaussian process regression under censoring

A key objective in engineering problems is to predict an unknown experim...
research
05/03/2023

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance

1. Species distribution models and maps from large-scale biodiversity da...

Please sign up or login with your details

Forgot password? Click here to reset