使用Sklearn排除一个交叉验证

Question

我试图使用交叉验证来测试我的分类器使用Sklearn。

我有三个班，总共有50个样本。

• 第1类有:5个样本
• 第2类有:15个样本
• 第三类有:30个样品。

如下运行的预期，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折叠来做独占，所以我做了以下几点，

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，我会得到上面的警告，这是很奇怪的。因为我认为当cv变得更大时，即使在计算上可能比较困难，结果也应该更准确。和我的推理有什么差距吗？为什么我会收到警告和错误？

在这种情况下，我如何才能正确地保留一次交叉验证？

Answer 1

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别中样本的分数不变。这可能是因为当褶皱的数量和样品的数量相同时，就会产生麻烦。你的版本是哪一种？这个错误消息肯定没有多大帮助。

顺便说一句，一般来说，我建议您对这么小的数据集使用StratifiedShuffleSplit。

编辑:当前版本提供了一个警告，这可能是一个错误：

is :399:警告:y中人口最少的类只有13个成员，这太少了。任何类的最小标签数都不能少于n_folds=68。% (min_labels，self.n_folds)，警告)