如何使用scikit-learn进行高斯/多项式回归? [英] How to do gaussian/polynomial regression with scikit-learn?

问题描述

scikit-learn是否提供使用高斯或多项式核执行回归的工具?我查看了API，但没有看到任何API. 有人在scikit-learn之上构建了一个软件包吗?

Does scikit-learn provide facility to perform regression using a gaussian or polynomial kernel? I looked at the APIs and I don't see any. Has anyone built a package on top of scikit-learn that does this?

推荐答案

使用支持向量回归sklearn.svm.SVR并设置适当的kernel(请参见

Either you use Support Vector Regression sklearn.svm.SVR and set the appropritate kernel (see here).

或者您安装sklearn的最新主版本并使用最近添加的sklearn.preprocessing.PolynomialFeatures(请参阅

Or you install the latest master version of sklearn and use the recently added sklearn.preprocessing.PolynomialFeatures (see here) and then OLS or Ridge on top of that.

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