我正在尝试从头开始编写SVM算法而不使用sklearn包，现在我想测试我的X_test和Y_predict的准确率得分。sklearn已经有了这个功能的函数：
clf.score(X_test,Y_predict)
现在，我追踪了sklearn包中的代码，我找不到’score’函数是如何从头编写的。
以及如何从sklearn的SVC生成模型：
SVM分类器 :: SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma=2, kernel='poly', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
在我拟合和训练数据集后，我希望模型能够生成，以便我可以使用Pickle保存和加载它。
回答：
如果你使用IPython，通常可以通过在函数后面添加??来查找函数的定义位置。例如：
>>> from sklearn.svm import SVC>>> svc = SVC()>>> svc.score??Signature: svc.score(X, y, sample_weight=None)Source: def score(self, X, y, sample_weight=None): """Returns the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters ---------- X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. Returns ------- score : float Mean accuracy of self.predict(X) wrt. y. """ from .metrics import accuracy_score return accuracy_score(y, self.predict(X), sample_weight=sample_weight)File: ~/miniconda/lib/python3.6/site-packages/sklearn/base.pyType: method在这种情况下，它来自ClassifierMixin，因此这段代码可以用于所有分类器。
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L310
https://ipython.readthedocs.io/en/stable/interactive/python-ipython-diff.html#accessing-help
