python中的KFold到底做什么？ [英] What does KFold in python exactly do?

问题描述

我正在看本教程： https：//www.dataquest .io / mission / 74 / getting-started-with-kaggle

我进入第9部分，进行了预测。在其中，有一个数据数据称为泰坦尼克号，然后使用以下方法将其分成折叠：

I got to part 9, making predictions. In there there is some data in a dataframe called titanic, which is then divided up in folds using:

# Generate cross validation folds for the titanic dataset.  It return the row indices corresponding to train and test.
# We set random_state to ensure we get the same splits every time we run this.
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)

<我不确定它到底在做什么，什么是kf对象。我尝试阅读文档，但并没有太大帮助。另外，有三折（n_folds = 3），为什么以后只在该行中访问火车和测试（我怎么知道它们被称为火车和测试）？

I am not sure what is it exactly doing and what kind of object kf is. I tried reading the documentation but it did not help much. Also, there are three folds (n_folds=3), why is it later only accessing train and test (and how do I know they are called train and test) in this line?

for train, test in kf:

推荐答案

KFold将提供训练/测试索引，以在训练和测试集中拆分数据。它将数据集分成连续的 k 折叠（默认不改组），然后每个折叠使用一次验证集，而 k-1 剩下的褶皱形成训练集（源 a>）。

KFold will provide train/test indices to split data in train and test sets. It will split dataset into k consecutive folds (without shuffling by default).Each fold is then used a validation set once while the k - 1 remaining folds form the training set (source).

比方说，您的数据索引为1到10。如果使用 n_fold = k ，在第一次迭代中，您将获得 i 个第（i <= k）倍作为测试索引，其余的（k-1）折叠（没有 i 倍）作为火车索引。

Let's say, you have some data indices from 1 to 10. If you use n_fold=k, in first iteration you will get i'th (i<=k) fold as test indices and remaining (k-1) folds (without that i'th fold) together as train indices.

示例

import numpy as np
from sklearn.cross_validation import KFold

x = [1,2,3,4,5,6,7,8,9,10,11,12]
kf = KFold(12, n_folds=3)

for train_index, test_index in kf:
    print (train_index, test_index)

输出

折叠1：[4 5 6 7 8 9 10 11] [0 1 2 3]

Fold 1: [ 4 5 6 7 8 9 10 11] [0 1 2 3]

折叠2：[0 1 2 3 8 9 10 11] [4 5 6 7]

Fold 2: [ 0 1 2 3 8 9 10 11] [4 5 6 7]

第三折：[0 1 2 3 4 5 6 7] [8 9 10 11]

Fold 3: [0 1 2 3 4 5 6 7] [ 8 9 10 11]

sklearn 0.20的导入更新：

KFold对象已移至 sklearn .model_selection 模块的版本为0.20。要在sklearn 0.20+中导入KFold，请使用sklearn.model_selection中的导入KFold 。 KFold当前文档源

KFold object was moved to the sklearn.model_selection module in version 0.20. To import KFold in sklearn 0.20+ use from sklearn.model_selection import KFold. KFold current documentation source

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