我试图使用交叉验证来使用Sklearn测试我的分类器。
我有三个班，总共50个样本。
一级有：5个样品
2类有：15个样品
3类有：30个样品
以下运行如预期，这大概是5倍交叉验证。
result = cross_validation.cross_val_score(classifier, X, y, cv=5)
我试着用cv=50倍来去掉一个，所以我做了以下的工作，
result = cross_validation.cross_val_score(classifier, X, y, cv=50)
然而，令人惊讶的是，它给出了以下错误：
/Library/Python/2.7/site-packages/sklearn/cross_validation.py:413: Warning: The least populated class in y has only 5 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=50.
% (min_labels, self.n_folds)), Warning)
/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/_methods.py:67: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
Traceback (most recent call last):
File "b.py", line 96, in <module>
scores1 = cross_validation.cross_val_score(classifier, X, y, cv=50)
File "/Library/Python/2.7/site-packages/sklearn/cross_validation.py", line 1151, in cross_val_score
for train, test in cv)
File "/Library/Python/2.7/site-packages/sklearn/externals/joblib/parallel.py", line 653, in __call__
self.dispatch(function, args, kwargs)
File "/Library/Python/2.7/site-packages/sklearn/externals/joblib/parallel.py", line 400, in dispatch
job = ImmediateApply(func, args, kwargs)
File "/Library/Python/2.7/site-packages/sklearn/externals/joblib/parallel.py", line 138, in __init__
self.results = func(*args, **kwargs)
File "/Library/Python/2.7/site-packages/sklearn/cross_validation.py", line 1240, in _fit_and_score
test_score = _score(estimator, X_test, y_test, scorer)
File "/Library/Python/2.7/site-packages/sklearn/cross_validation.py", line 1296, in _score
score = scorer(estimator, X_test, y_test)
File "/Library/Python/2.7/site-packages/sklearn/metrics/scorer.py", line 176, in _passthrough_scorer
return estimator.score(*args, **kwargs)
File "/Library/Python/2.7/site-packages/sklearn/base.py", line 291, in score
return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
File "/Library/Python/2.7/site-packages/sklearn/neighbors/classification.py", line 147, in predict
neigh_dist, neigh_ind = self.kneighbors(X)
File "/Library/Python/2.7/site-packages/sklearn/neighbors/base.py", line 332, in kneighbors
return_distance=return_distance)
File "binary_tree.pxi", line 1307, in sklearn.neighbors.kd_tree.BinaryTree.query (sklearn/neighbors/kd_tree.c:10506)
File "binary_tree.pxi", line 226, in sklearn.neighbors.kd_tree.get_memview_DTYPE_2D (sklearn/neighbors/kd_tree.c:2715)
File "stringsource", line 247, in View.MemoryView.array_cwrapper (sklearn/neighbors/kd_tree.c:24789)
File "stringsource", line 147, in View.MemoryView.array.__cinit__ (sklearn/neighbors/kd_tree.c:23664)
ValueError: Invalid shape in axis 0: 0.
另外，另一件奇怪的事情是，当我做cv=5时，我没有收到任何警告。当我做cv=50时，我得到了上面的警告，这很奇怪。因为我认为当cv变大时，尽管计算起来可能会更困难，但结果应该更准确。我的推理有什么分歧吗？为什么我会得到警告和错误？
在这种情况下，我怎样才能正确地排除交叉验证？
最佳答案
默认情况下，用于分类的cv=5进行分层5倍交叉验证。
这意味着它试图保持一个类中样本的分数不变。当折叠次数与样本数目相同时，这可能会导致问题。
你在哪个版本？
这条错误信息肯定没有多大帮助。
顺便说一下，一般来说，我建议您对这样一个小的数据集使用StratifiedShuffleSplit。
[编辑]：当前版本发出警告，这可能是一个错误：
sklearn/cross_validation.py:399:警告：y中填充最少的类只有13个成员，这太少了。任何类别的最小标签数不能小于n_folds=68。
%（最小标签，自我折叠），警告）
关于python - 使用Sklearn进行一次交叉验证，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/29476807/