Question

我正在尝试使用交叉验证来使用Sklearn测试我的分类器。

我有3个班级，总共50个样本。

第1类有：5个样本
第2类有：15个样本
第3类有：30个样本

以下按预期运行，大概是进行5倍交叉验证。

result = cross_validation.cross_val_score(classifier, X, y, cv=5)

我正在尝试使用cv = 50折叠进行一次性离开，所以我执行以下操作，

result = cross_validation.cross_val_score(classifier, X, y, cv=50)

然而，令人惊讶的是，它会出现以下错误：

/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，另一个奇怪的事情是，当我做cv = 5时，我没有得到任何警告。当我做cv = 50时，我得到了上述警告，这很奇怪。因为我认为当cv变大时，即使它可能在计算上更难，但结果应该更准确。我的推理有什么差距吗？为什么我会收到警告和错误？

如何正确地在这种情况下进行一次性交叉验证？

Answer 1

默认情况下，cv = 5进行分类会对5倍交叉验证进行分层。这意味着它试图保持一个类的样本分数不变。当折叠数与样本数相同时，这可能会导致麻烦。你在哪个版本？此错误消息肯定不是很有帮助。

顺便说一下，我建议您使用StratifiedShuffleSplit来表示这么小的数据集。

[edit]：当前版本发出警告，该错误应该是错误：

sklearn / cross_validation.py:399：警告：y中填充最少的类只有13个成员，这个成员太少了。任何类的最小标签数不能少于n_folds = 68。％（min_labels，self.n_folds）），警告）

使用Sklearn留出一个交叉验证

1 个答案: