【发布时间】:2016-02-17 13:18:57
【问题描述】:
在scikit-learn 中运行交叉验证时,所有分类器都会有一个工厂函数score(),我可以轻松检查分类器的准确性,例如来自http://scikit-learn.org/stable/modules/cross_validation.html
>>> import numpy as np
>>> from sklearn import cross_validation
>>> from sklearn import datasets
>>> from sklearn import svm
>>> iris = datasets.load_iris()
>>> iris.data.shape, iris.target.shape
((150, 4), (150,))
>>> X_train, X_test, y_train, y_test = cross_validation.train_test_split(
... iris.data, iris.target, test_size=0.4, random_state=0)
>>> X_train.shape, y_train.shape
((90, 4), (90,))
>>> X_test.shape, y_test.shape
((60, 4), (60,))
>>> clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
>>> clf.score(X_test, y_test)
0.96...
在挖掘scikit-learn 的github repo 之后,我仍然无法弄清楚clf.score() 函数的函数在哪里。
有此链接,但不包含score()、https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/classes.py
sklearn 分类器的 score() 函数位于何处?
我可以implement my own score function easily,但目的是构建我的库,使其与sklearn 分类器保持一致,不是想出我自己的评分函数 =)
【问题讨论】:
标签: python function machine-learning scikit-learn cross-validation