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]
我不知道为什么在执行时：...＃将数据集拆分为两个相等的部分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 error in the line below
clf.fit(X_train, y_train, cv=5)
...
我收到此错误：
Traceback (most recent call last):
File "C:\Python27\SVMpredictCROSSeGRID.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:\Python27\lib\site-packages\sklearn\grid_search.py", line 354, in fit
return self._fit(X, y)
File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 372, in _fit
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
File "C:\Python27\lib\site-packages\sklearn\cross_validation.py", line 1148, in check_cv
cv = StratifiedKFold(y, cv, indices=is_sparse)
File "C:\Python27\lib\site-packages\sklearn\cross_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.
2 回答
如果不能拆分测试和训练集，每个类中填充的每个类都足够，那么请尝试更新scikit库 .
pip install -U scikit-learn您将获得与警告相同的消息，以便您可以运行代码 .
该算法要求训练集中的标签至少有3个实例 . 虽然您的
target数组包含每个标签的至少3个实例，但是当您在训练和测试之间拆分数据时，并非所有训练标签都有3个实例 .您需要合并某些类标签或增加训练样本以解决问题 .