svc.decision_function

svc.decision_function是SVM分类器中的一个方法，用于计算每个样本点到分类超平面的距离，可以用来判断样本点的分类情况。具体来说，对于二分类问题，如果decision_function的值大于，则该样本点属于正类，否则属于负类。对于多分类问题，decision_function返回的是每个样本点到各个分类超平面的距离，最终分类结果取距离最大的那个类别。

相关问题

SVC(decision_function_shape='ovo')

SVC是支持向量机（Support Vector Machine）的缩写，它是一种二分类模型，可以用于线性和非线性分类、回归以及异常值检测。decision_function_shape参数用于指定多分类问题的决策函数形状，'ovo'表示采用一对一（One-vs-One）策略，即将多类别问题转化为多个二分类问题，再将结果综合起来。另外一个选项是'ovr'，表示采用一对多（One-vs-Rest）策略，即将多类别问题转化为多个二分类问题，每个二分类问题都是某一个类别与其他所有类别的二分类问题，最终将所有二分类问题的结果进行统计。默认情况下，decision_function_shape参数取值为'ovr'。

SVC.feature_importances_

As an AI language model, I don't have access to the specific code or data you are working with, so I cannot provide you with an exact answer. However, in general, the attribute `feature_importances_` is not available for Support Vector Machine (SVM) classifiers in Scikit-learn because SVMs do not inherently provide a feature importance metric. This attribute is only available for certain tree-based models, such as Random Forest and Decision Trees.

If you are interested in obtaining feature importances for an SVM, one approach is to use a permutation-based feature importance method. This involves randomly permuting the values of each feature and measuring the decrease in model performance. The features with the largest decrease in performance after permutation are considered to be the most important.

Another approach is to use a linear SVM and look at the coefficients of the linear function learned by the model. The magnitude of the coefficients can be used as a proxy for the importance of the corresponding feature. However, this approach assumes that the relationship between the features and the target variable is linear, which may not be the case in practice.