sklearn中的线性回归

在sklearn中，线性回归模型可以使用`sklearn.linear_model.LinearRegression`类来实现。该类有几个可选参数，包括`fit_intercept`、`normalize`、`copy_X`和`n_jobs`。其中，`fit_intercept`参数表示是否计算截距，`normalize`参数表示是否对特征进行归一化，`copy_X`参数表示是否复制输入数据，`n_jobs`参数表示并行计算时使用的核心数量。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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- *1* *3* [sklearn-线性回归](https://blog.csdn.net/weixin_58975360/article/details/124284392)[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^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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