I am using XGBoost (gradient boosting) to predict the value of a continuous dependent variable
The figure below shows a histogram of both the dependent variable data and the predicted data. (blue is the original dependent value and orange is the predicted).
Are there any methods/literature that talk about proper scaling or adjusting/mapping the output of a regression so that it can better relate to the original dependent variable?
For example the first histogram bar (from 0-0.25) for the dependent variable, what values of the predictions approximately correspond to that range?
For the sake of context the data is for credit risk scoring, for example 0 represents someone who has never made a payment and is in complete delinquency and 3 is someone with perfect credit. Then there are the individuals in between.