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.