支持向量机（SVM）训练后如何提取完整的模型信息？答案

【问题标题】：How to extract full model information after Support Vector Machine (SVM) training?支持向量机（SVM）训练后如何提取完整的模型信息？
【发布时间】：2016-07-18 17:08:07
【问题描述】：

我正在使用“sklearn.svm”进行基本的 SVM 训练。对于 SVC，有没有办法打印出文档中描述的模型详细信息：

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

注意：我不是在谈论可以通过“get_params”或“set_params”获得的参数。我指的是作为算法结果确定的实际系数。

【问题讨论】：

标签： scikit-learn svm

【解决方案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.

您可以从此属性派生有关您模型的所有信息。

例如：clf.n_support_将返回n_support_您的模型。

【讨论】：