Scikit学习SVC决策功能并进行预测 [英] Scikit Learn SVC decision_function and predict

问题描述

我正在尝试理解SVC的实例方法Decision_function和预测之间的关系(

I'm trying to understand the relationship between decision_function and predict, which are instance methods of SVC (http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html). So far I've gathered that decision function returns pairwise scores between classes. I was under the impression that predict chooses the class that maximizes its pairwise score, but I tested this out and got different results. Here's the code I was using to try and understand the relationship between the two. First I generated the pairwise score matrix, and then I printed out the class that has maximal pairwise score which was different than the class predicted by clf.predict.

        result = clf.decision_function(vector)[0]
        counter = 0
        num_classes = len(clf.classes_)
        pairwise_scores = np.zeros((num_classes, num_classes))
        for r in xrange(num_classes):
            for j in xrange(r + 1, num_classes):
                pairwise_scores[r][j] = result[counter]
                pairwise_scores[j][r] = -result[counter]
                counter += 1

        index = np.argmax(pairwise_scores)
        class = index_star / num_classes
        print class
        print clf.predict(vector)[0]

有人知道这些预测功能和决策功能之间的关系吗?

Does anyone know the relationship between these predict and decision_function?

推荐答案

我不完全理解您的代码，但让我们来看一下您引用的文档页面的示例:

I don't fully understand your code, but let's go trough the example of the documentation page you referenced:

import numpy as np
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
from sklearn.svm import SVC
clf = SVC()
clf.fit(X, y) 

现在让我们将决策函数和预测应用于样本:

Now let's apply both the decision function and predict to the samples:

clf.decision_function(X)
clf.predict(X)

我们得到的输出是:

array([[-1.00052254],
       [-1.00006594],
       [ 1.00029424],
       [ 1.00029424]])
array([1, 1, 2, 2])

这很容易解释:desion函数告诉我们分类器生成的超平面在哪一侧(以及距离我们有多远).根据这些信息，估算器会使用相应的标签标记示例.

And that is easy to interpret: The desion function tells us on which side of the hyperplane generated by the classifier we are (and how far we are away from it). Based on that information, the estimator then label the examples with the corresponding label.

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