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?
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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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