sklearn.linear_model.LinearRegression (2024)

class sklearn.linear_model.LinearRegression(*, fit_intercept=True, copy_X=True, n_jobs=None, positive=False)[source]¶

Ordinary least squares Linear Regression.

LinearRegression fits a linear model with coefficients w = (w1, …, wp)to minimize the residual sum of squares between the observed targets inthe dataset, and the targets predicted by the linear approximation.

Parameters:

fit_interceptbool, default=True

Whether to calculate the intercept for this model. If setto False, no intercept will be used in calculations(i.e. data is expected to be centered).

copy_Xbool, default=True

If True, X will be copied; else, it may be overwritten.

n_jobsint, default=None

The number of jobs to use for the computation. This will only providespeedup in case of sufficiently large problems, that is if firstlyn_targets > 1 and secondly X is sparse or if positive is setto True. None means 1 unless in ajoblib.parallel_backend context. -1 means using allprocessors. See Glossary for more details.

positivebool, default=False

When set to True, forces the coefficients to be positive. Thisoption is only supported for dense arrays.

Added in version 0.24.

Attributes:

coef_array of shape (n_features, ) or (n_targets, n_features)

Estimated coefficients for the linear regression problem.If multiple targets are passed during the fit (y 2D), thisis a 2D array of shape (n_targets, n_features), while if onlyone target is passed, this is a 1D array of length n_features.

rank_int

Rank of matrix X. Only available when X is dense.

singular_array of shape (min(X, y),)

Singular values of X. Only available when X is dense.

intercept_float or array of shape (n_targets,)

Independent term in the linear model. Set to 0.0 iffit_intercept = False.

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when Xhas feature names that are all strings.

Added in version 1.0.

See also

Ridge: Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization.
Lasso: The Lasso is a linear model that estimates sparse coefficients with l1 regularization.
ElasticNet: Elastic-Net is a linear regression model trained with both l1 and l2 -norm regularization of the coefficients.

Notes

From the implementation point of view, this is just plain OrdinaryLeast Squares (scipy.linalg.lstsq) or Non Negative Least Squares(scipy.optimize.nnls) wrapped as a predictor object.

Examples

>>> import numpy as np>>> from sklearn.linear_model import LinearRegression>>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])>>> # y = 1 * x_0 + 2 * x_1 + 3>>> y = np.dot(X, np.array([1, 2])) + 3>>> reg = LinearRegression().fit(X, y)>>> reg.score(X, y)1.0>>> reg.coef_array([1., 2.])>>> reg.intercept_3.0...>>> reg.predict(np.array([[3, 5]]))array([16.])

Methods

fit(X,y[,sample_weight])	Fit linear model.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the linear model.
score(X,y[,sample_weight])	Return the coefficient of determination of the prediction.
set_fit_request(*[,sample_weight])	Request metadata passed to the `fit` method.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[,sample_weight])	Request metadata passed to the `score` method.