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.
