Explaining the Performance of Black Box Regression Models

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Areosa, I (Author) Other; Torgo, L (Author) FCUP

Conference proceedings International

Title: 2019 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2019)

Pages: 110-118

6th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Washington, DC, OCT 05-08, 2019

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Other information

Authenticus ID: P-00R-QTS

DOI: 10.1109/dsaa.2019.00025

Abstract (EN): The widespread usage of Machine Learning and Data Mining models in several key areas of our societies has raised serious concerns in terms of accountability and ability to justify and interpret the decisions of these models. This is even more relevant when models are too complex and often regarded as black boxes. In this paper we present several tools designed to help in understanding and explaining the reasons for the observed predictive performance of black box regression models. We describe, evaluate and propose several variants of Error Dependence Plots. These plots provide a visual display of the expected relationship between the prediction error of any model and the values of a predictor variable. They allow the end user to understand what to expect from the models given some concrete values of the predictor variables. These tools allow more accurate explanations on the conditions that may lead to some failures of the models. Moreover, our proposed extensions also provide a multivariate perspective of this analysis, and the ability to compare the behaviour of multiple models under different conditions. This comparative analysis empowers the end user with the ability to have a case-based analysis of the risks associated with different models, and thus select the model with lower expected risk for each test case, or even decide not to use any model because the expected error is unacceptable.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9