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 ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
