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Gents, I'm predicting a float column that has distribution pretty much like below.
So there are different approaches that I'm testing (I'm really interested mostly in the direction and magnitude, not precise values).
split the target into classes like {-50,-40,30,20,10, 10,20,30,40,50} - and make 10 binary columns, do multiclassification within 1 model.
just assign the categories within one column {-50,-40,30,20,10, 0, 10,20,30,40,50} and forecast the category using 1 column.
make 1 distinct binary model per each category (as in classic methods before multiclassification existed).
Now I'm trying different variations and also different splittage of classes. However, is there a catboost official guideline which method is the best with this package? Been watching videos, reading some docs, but haven't really found an answer...
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Gents, I'm predicting a float column that has distribution pretty much like below.
So there are different approaches that I'm testing (I'm really interested mostly in the direction and magnitude, not precise values).
split the target into classes like {-50,-40,30,20,10, 10,20,30,40,50} - and make 10 binary columns, do multiclassification within 1 model.
just assign the categories within one column {-50,-40,30,20,10, 0, 10,20,30,40,50} and forecast the category using 1 column.
make 1 distinct binary model per each category (as in classic methods before multiclassification existed).
Now I'm trying different variations and also different splittage of classes. However, is there a catboost official guideline which method is the best with this package? Been watching videos, reading some docs, but haven't really found an answer...
Thanks!
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