The following example explains how to do it using the numpy.vander function:
http://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html
Mathieu
On Wed, Aug 8, 2012 at 11:27 AM, Zach Bastick <[email protected]>wrote:
> How can you increase the degree of the polynomial for multivariate
> LinearRegression?
> Numpy.polyfit has a "deg" parameter, allowing you to choose the degree
> of the fitting polynomial, but doesn't work with multivariate data:
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html
>
> For example, a 2nd degree polynomial fit would have the following
> regression equation:
> y = intercept + (b*x1 +b* x1^2) + (b*x2 + b*x2^2) + (b*x3 + b*x3^2)
>
> The following only does 1st degree polynomial fit:
>
> clf = linear_model.LinearRegression()
> clf.fit(x,y)
> regress_coefs = clf.coef_
> regress_intercept = clf.intercept_
>
> So how can you do higher degree polynomial fits? This should allow me to
> getting a better fitting curve to get a prediction/regression formula
> for my machine learning project.
>
> Thanks,
>
> Zach
>
>
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