Distribution Assertive Regression

TL;DR

This paper introduces a novel regression approach that models errors across different quantiles of the target variable, improving fit and interpretability over traditional single-line methods.

Contribution

The paper proposes a distribution assertive regression method that accounts for varying behaviors across quantiles, enhancing regression accuracy and explainability.

Findings

Improved fit across different quantiles of data

Enhanced interpretability of regression models

Significant reduction in error eccentricity

Abstract

In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target variable usually have a varying behavior across the various quantiles of the dependent variable and hence single point diagnostic like MAPE has its limitation to signify the level of fitness across the distribution of Y(dependent variable). To address this problem, a novel approach is proposed in the paper to deal with regression fitting over various quantiles of target variable. Using this approach we have significantly improved the eccentric behavior of the distance (error) between predicted and actual value of regression. Our proposed solution is based on understanding the segmented behavior of the data with respect to the internal segments within the data and approach for retrospectively fitting the data based on each quantile behavior. We believe exploring and using this approach would help in achieving better and more explainable results in most settings of real world data modelling problems.