我在每个类中都有三个以上的元素，但我得到这个错误："class cannot be less than k=3 in scikit-learn"

Question

这是我的目标（y）：

target = [7,1,2,2,3,5,4,
      1,3,1,4,4,6,6,
      7,5,7,8,8,8,5,
      3,3,6,2,7,7,1,
      10,3,7,10,4,10,
      2,2,2,7]

我不知道为什么当我执行：

...
# Split the data set in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=0)
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                 'C': [1, 10, 100, 1000]},
                {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = ['precision', 'recall']
for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()
    clf = GridSearchCV(SVC(C=1), tuned_parameters)#scoring non esiste
    # I get an error in the line below
    clf.fit(X_train, y_train, cv=5)
...

我得到这个错误：

Traceback (most recent call last):
  File "C:Python27SVMpredictCROSSeGRID.py", line 232, in <module>
clf.fit(X_train, y_train, cv=5)  #The minimum number of labels for any class cannot be less than k=3.
File "C:Python27libsite-packagessklearngrid_search.py", line 354, in fit
return self._fit(X, y)
File "C:Python27libsite-packagessklearngrid_search.py", line 372, in _fit
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
File "C:Python27libsite-packagessklearncross_validation.py", line 1148, in check_cv
cv = StratifiedKFold(y, cv, indices=is_sparse)
File "C:Python27libsite-packagessklearncross_validation.py", line 358, in __init__
" be less than k=%d." % (min_labels, k))
ValueError: The least populated class in y has only 1 members, which is too few. The minimum number of labels for any class cannot be less than k=3.

Answer 1

算法要求在训练集中至少有3个标签实例。虽然target数组至少包含每个标签的3个实例，但在训练和测试之间划分数据时，并非所有训练标签都有3个实例。

您要么需要合并一些类标签，要么增加训练样本来解决问题。

Answer 2

如果您不能在每个折叠中足够填充每个类的情况下拆分测试和训练集，那么请尝试更新Scikit库。

pip install -U scikit-learn

您将得到与警告相同的消息，因此您可以运行代码。