Question

我想使用Leave one out cross validation。但我得到以下错误：

AttributeError                            Traceback (most recent call last)
<ipython-input-19-f15f1e522706> in <module>()
      3 loo = LeaveOneOut(num_of_examples)
      4 #loo.get_n_splits(X_train_std)
----> 5 for train, test in loo.split(X_train_std):
      6     print("%s %s" % (train, test))

AttributeError：＆＃39; LeaveOneOut＆＃39;对象没有属性＆＃39; split＆＃39;

详细代码如下：

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = 
train_test_split(X, y, test_size=0.3, random_state=0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

from sklearn.cross_validation import LeaveOneOut
num_of_examples = len(X_train_std)
loo = LeaveOneOut(num_of_examples)
for train, test in loo.split(X_train_std):
print("%s %s" % (train, test))

Answer 1

我认为您使用的scikit-learn版本低于0.18，可能会参考0.18版本的一些教程。

在0.18之前的版本中，LeaveOneOut()构造函数具有必需参数n，在您发布的上述代码中未提供该参数。因此错误。您可以参考其中提及的documentation of LeaveOneOut for version 0.17 here：

参数： n ：int数据集中的元素总数。

解决方案：

更新scikit-learn to version 0.18
按如下方式初始化 LeaveOneOut ：

loo = LeaveOneOut(size of X_train_std)

修改：

如果您使用的是scikit版本＆gt; = 0.18：

from sklearn.model_selection import LeaveOneOut for train_index, test_index in loo.split(X): print("%s %s" % (train_index, test_index)) X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index]

否则，对于版本＆lt; 0.18使用这样的迭代（请注意，此处loo.split()未使用，loo直接使用）：

from sklearn.cross_validation import LeaveOneOut loo = LeaveOneOut(num_of_examples) for train_index, test_index in loo: print("%s %s" % (train_index, test_index)) X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index]

Answer 2

使用sklearn.model_selection导入train_test_split 而不是cross_validation，因为将cross_validation更改为model_selction

交叉验证问题

2 个答案: