Treating continuous data as a classification problem by predicting bins or quintiles I currently have a model that has several numeric Y or predicted variables
Sample Data:




Y1
Y2
...
YN




2710
0.32
...
31231


1710
0.52
...
51231




I am currently using regression (multioutput regression with gradient boosting) to predict the Y variables.
Currently I am not happy with the accuracy and was looking at ways to increase accuracy. I was recommended using discretization so I treat Y as bins or quintiles. i.e:




Y1
Y2
...
YN




2000 - 3000
0.3 - 0.4
...
30000 - 40000


1000 - 2000
0.5 - 0.6
...
51000 - 50000




How can I go about doing this? What algorithms or libraries perform this? I am finding online how to discretize features but not so much the data itself. Do I need to manually shape the trained data into bins and then train on it?
I guess what I'm asking is where to go from here coding wise.
One option is scikit-learn's KBinsDiscretizer which bins continuous data into intervals.