如何在支持向量机（SVM）培训之后提取完整模型信息？

Question

我正在使用“sklearn.svm”来进行基本的SVM培训。对于SVC，有没有办法打印出的文档中描述的模型细节：

from sklearn.svm import SVC 
clf = SVC(C=10.0,kernel='linear',probability=True,verbose=True) 
clf.fit(X, y_) 

注意：我说的不是可以用“get_params”或“set_params”要达到的参数。我指的是作为算法结果确定的实际系数。

Answer 1

从SVC的文档：

Attributes: 

support_ : array-like, shape = [n_SV] 
    Indices of support vectors. 
    support_vectors_ : array-like, shape = [n_SV, n_features] 
    Support vectors. 

n_support_ : array-like, dtype=int32, shape = [n_class] 
    Number of support vectors for each class. 

dual_coef_ : array, shape = [n_class-1, n_SV] 
    Coefficients of the support vector in the decision function. For  
    multiclass, coefficient for all 1-vs-1 classifiers. The layout of 
    the coefficients in the multiclass case is somewhat non-trivial. 
    See the section about multi-class classification in the SVM 
    section of the User Guide for details. 

coef_ : array, shape = [n_class-1, n_features] 

     Weights assigned to the features (coefficients in the primal 
     problem). This is only available in the case of a linear 
     kernel. 

     coef_ is a readonly property derived from dual_coef_ and 
     support_vectors_. 

intercept_ : array, shape = [n_class * (n_class-1)/2] 
     Constants in decision function. 

你可以得到你从这个属性有关模型中的所有信息。

例如：clf.n_support_将返回您的模型的n_support_。