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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.

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One option is scikit-learn's KBinsDiscretizer which bins continuous data into intervals.

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