  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.
