sklearn.linearn

_Regression

The sklearn.linear_regression module provides functions for linear regression. Linear regression is a supervised learning algorithm used to predict a continuous output variable (y) based on one or more input variables (x). The module provides functions for simple linear regression (where there is only one input variable) and multiple linear regression (where there are multiple input variables).

Some of the functions provided by the module are:

- LinearRegression: This class provides a simple interface for performing linear regression. It fits a linear model to the input data and can be used to make predictions on new data.

- Ridge: This class provides a way to perform linear regression with L2 regularization. This can be used to prevent overfitting in cases where there are many input variables.

- Lasso: This class provides a way to perform linear regression with L1 regularization. This can be used to perform feature selection, as it tends to set the coefficients of irrelevant input variables to zero.

- ElasticNet: This class provides a way to perform linear regression with a combination of L1 and L2 regularization. This can be used to balance the benefits of each type of regularization.

- SGDRegressor: This class provides a way to perform linear regression using stochastic gradient descent. This can be useful for very large datasets, as it can be more computationally efficient than other methods.

Overall, the sklearn.linear_regression module provides a variety of tools for performing linear regression and can be a useful tool for predictive modeling.