多分类LogisticRegression

我今天遇到了一个非常愚蠢的问题，就是关于sequential feature selection以及logistic regression多分类问题的情况.

我一直记着sfs是多分类的。但是今天找了半天翻了历史记录也没有找到。后来才发现原来不是sfs的问题，而是logisticRegression。只有在logisticRegression才会有是不是多分类的问题啊，好愚蠢。

今天跑特征筛选，

Logistic Regression (aka logit, MaxEnt) classifier.

In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the cross- entropy loss if the ‘multi_class’ option is set to ‘multinomial’. (Currently the ‘multinomial’ option is supported only by the ‘lbfgs’, ‘sag’ and ‘newton-cg’ solvers.)

This class implements regularized logistic regression using the ‘liblinear’ library, ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied).

The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with primal formulation. The ‘liblinear’ solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty.