Distribution Assertive Regression
This addresses the issue of poor model fitness across the distribution of dependent variables in regression for real-world data modelling, though it appears incremental as it builds on existing quantile-based methods.
The paper tackles the problem of regression models fitting all data with a single line, which leads to varying error behavior across quantiles of the dependent variable, limiting diagnostics like MAPE. The proposed novel approach fits regression over various quantiles, significantly improving the eccentric behavior of error between predicted and actual values.
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.