linear_model.linearregression

LinearRegression 是 scikit-learn 库中的一种线性回归模型。它使用最小二乘法来预测目标变量和一组自变量之间的关系。 LinearRegression 模型可以用来处理多元线性回归问题，也可以用来解决单变量线性回归问题。

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sklearn.linear_model.linearregression

sklearn.linear_model.linearregression是scikit-learn库中的一个线性回归模型，用于建立线性回归模型并进行预测。该模型基于最小二乘法，通过拟合训练数据中的线性关系来预测目标变量。它可以用于单变量线性回归和多变量线性回归，并提供了一些方法来评估模型的性能和进行特征选择。

linear_model.LinearRegression()

The `LinearRegression()` function is a class in the `linear_model` module of the popular Python library `scikit-learn`. This class implements a linear regression model, which is a supervised learning algorithm used for predicting continuous numerical values based on a set of input features.

The `LinearRegression()` function takes several optional arguments, such as `fit_intercept` (a boolean value indicating whether or not to include an intercept term in the model), `normalize` (a boolean value indicating whether or not to normalize the input features), and `copy_X` (a boolean value indicating whether or not to copy the input features before fitting the model).

After creating an instance of the `LinearRegression()` class, you can fit the model to your data using the `fit()` method, which takes two arguments: `X` (a 2D array or matrix of input features) and `y` (a 1D array of target values). Once the model is fitted, you can use the `predict()` method to make predictions on new data.