Interpreting Complex Regression Models

TL;DR

This paper introduces a model interpretation method for complex regression models that provides explanations grounded in actual learning examples, aiding feature understanding and debugging.

Contribution

The paper presents a novel interpretation approach that links explanations directly to training examples, enhancing interpretability of complex models.

Findings

Method effectively interprets complex regression models

Validates approach on music year prediction and mail churn datasets

Provides explanations grounded in actual training examples

Abstract

Interpretation of a machine learning induced models is critical for feature engineering, debugging, and, arguably, compliance. Yet, best of breed machine learning models tend to be very complex. This paper presents a method for model interpretation which has the main benefit that the simple interpretations it provides are always grounded in actual sets of learning examples. The method is validated on the task of interpreting a complex regression model in the context of both an academic problem -- predicting the year in which a song was recorded and an industrial one -- predicting mail user churn.