Xtrain, Xtest, Ytrain, Ytest = train_test_split

train_test_split函数的作用是将数据集按照一定比例划分为训练集和测试集，并且在划分的过程中保持了原始数据集中各个类别的分布比例。在这个例子中，Xtrain和Ytrain是训练集的特征和目标标签，Xtest和Ytest是测试集的特征和目标标签。具体的划分比例可以通过设置test_size参数来控制，通常情况下，训练集的比例会设置为0.6或0.7，测试集的比例则为1减去训练集比例。同时，使用stratify参数可以保持划分之后的训练集和测试集中各个类别的分布比例与原始数据集相同。 <span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *3* [关于x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)](https://blog.csdn.net/qq_42671928/article/details/121796069)[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_2"}}] [.reference_item style="max-width: 50%"]
- *2* [decisionTreeBreastcanserDataset.py](https://download.csdn.net/download/aotomo740/12416014)[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_2"}}] [.reference_item style="max-width: 50%"]
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