Predictability Analysis of Regression Problems via Conditional Entropy Estimations

TL;DR

This paper introduces reliable conditional entropy estimators to analyze the predictability of regression problems, providing a new framework to assess the potential performance of feature sets beyond traditional error metrics.

Contribution

It develops and enhances conditional entropy estimators, specifically KNIFE-P and LMC-P, to evaluate predictability in regression, extending analysis to the coefficient of determination R^2.

Findings

KNIFE-P and LMC-P estimators effectively capture predictability limits.

The estimators are robust across synthesized and real-world datasets.

Extended analysis improves interpretability of regression predictability.

Abstract

In the field of machine learning, regression problems are pivotal due to their ability to predict continuous outcomes. Traditional error metrics like mean squared error, mean absolute error, and coefficient of determination measure model accuracy. The model accuracy is the consequence of the selected model and the features, which blurs the analysis of contribution. Predictability, in the other hand, focus on the predictable level of a target variable given a set of features. This study introduces conditional entropy estimators to assess predictability in regression problems, bridging this gap. We enhance and develop reliable conditional entropy estimators, particularly the KNIFE-P estimator and LMC-P estimator, which offer under- and over-estimation, providing a practical framework for predictability analysis. Extensive experiments on synthesized and real-world datasets demonstrate the robustness and utility of these estimators. Additionally, we extend the analysis to the coefficient of determination \(R^2 \), enhancing the interpretability of predictability. The results highlight the effectiveness of KNIFE-P and LMC-P in capturing the achievable performance and limitations of feature sets, providing valuable tools in the development of regression models. These indicators offer a robust framework for assessing the predictability for regression problems.