I think it might generate a basis that is capable of generating what you describe above, but feature expansion concretely reads as
1, a, b, c, a ** 2, ab, ac, b ** 2, bc, c ** 2, a ** 3, a ** 2 * b, a ** 2 * c, a* b ** 2, abc, a*c**2, b**3, b**2 * c, b*c**2, c**3 Hope this helps On Fri, Nov 22, 2019 at 8:50 AM Sole Galli <solegal...@gmail.com> wrote: > Hello team, > > Can I double check with you that I understand correctly what the > PolynomialFeatures() is doing under the hood? > > If I set it like this: > > poly = PolynomialFeatures(degree=3, interaction_only=False, > include_bias=False) > > and I fit it on a dataset with 3 variables, a,b and c. > > Am I correct to say that the fit() method creates all possible > combinations like this: > a; > b; > c; > (a+b)^2 > (a+b)^3 > (a+c)^2 > (a+c)^3 > (c+b)^2 > (c+b)^3 > (a+b+c)^2 > (a+b+c)^3 > > And the transform() generates the expansion, without the constant that > multiplies the interactions and avoiding duplicated terms after the > expansion? > > Thanks for the help. > > Kind regards > > Sole > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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