Hi Michael, Nicolas, Thank you both, that is very helpful!
Best wishes Sole On Sun, 24 Nov 2019 at 03:37, Michael Eickenberg < michael.eickenb...@gmail.com> wrote: > 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 >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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