from sklearn.model_selection import train_test_split说No module named 'sklearn'

出现No module named 'sklearn'的错误是由于你的scikit-learn版本过低导致的。解决此问题的方法有两种：
方法一：运行cmd，输入命令：conda update scikit-learn，然后按提示输入y。
方法二：运行cmd，直接输入命令：pip install -U scikit-learn。

可能出现的问题：
1. 如果在Windows下输入pip install -U scikit-learn后出现time out的错误，这是由于pip默认使用国外源，速度较慢容易超时。解决办法是在C:\Users\Administrator\AppData\Roaming文件夹下创建一个pip文件夹，然后在pip文件夹下新建一个文件pip.ini，并将超时时间修改为60000，同时更改为国内清华源下载。具体内容如下：
[global]
timeout = 60000
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
[install]
use-mirrors = true
mirrors = https://pypi.tuna.tsinghua.edu.cn

2. 如果出现OSError: [WinError 17] 系统无法将文件移到不同的磁盘驱动器的错误，原因是文件被占用，系统无法复制文件。解决办法是关闭Python（或Jupyter、Pycharm）和相关进程，或重新启动电脑，然后重新运行cmd，输入命令：pip install -U scikit-learn。

相关问题

from sklearn.model_selection import train_test_split

从sklearn库中导入train_test_split函数的作用是将数据集划分为训练集和测试集。通过调用该函数，可以将特征数据集X和目标数据集y按照指定的比例进行划分，并返回划分后的四个数据集：训练集特征数据集X_train、测试集特征数据集X_test、训练集目标数据集y_train和测试集目标数据集y_test。其中，参数test_size表示测试集占总样本的比例，random_state是随机种子用于产生随机划分，shuffle表示是否在划分前对数据进行洗牌操作。引用<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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from sklearn.cross_validation import train_test_split

在进行cross-validation时，如果导入"from sklearn.cross_validation import train_test_split"，会出现一个DeprecationWarning（弃用警告）。在sklearn的0.18版本中，cross_validation模块已经被弃用，现在应该使用model_selection模块代替。因此，你可以将导入语句修改为"from sklearn.model_selection import train_test_split"。这样，你就可以成功导入train_test_split函数。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [from sklearn.cross_validation import train_test_split](https://blog.csdn.net/c_j_k/article/details/78820851)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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