Particular regressors may make assumptions about the distribution of y, or its relationship with the features X. You should be aware of those assumptions and reason about whether they are held well enough. A TransformedTargetRegressor may be used to make your target better match those assumptions, e.g. by trying to predict the logarithm or power transform of the original targets, but again you need to look at the distribution of y and the assumptions of the regressor.
On Wed, 30 Jan 2019 at 21:44, lampahome <pahome.c...@mirlab.org> wrote: > I found many cases in kaggle to predict the quantity or trends. They all > set the real quantity as y. > > But I have question is that does anyone set the changing ratio as y? > > Like: > > X y > Day1 0.2 > Day2 0.1 > Day3 0.15 > Day4 -0.1 > > y is the changing ratio compared with previous day. > > Why anybody set the real quantity(ex: sales, car numbers...etc) as y > rather than changing ratio as y? > > I want to know it's based on experience or other reasons > > thx > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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