# mars.learn.linear_model.LinearRegression¶

class mars.learn.linear_model.LinearRegression(*, fit_intercept=True, normalize=False, copy_X=True, 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 in the dataset, and the targets predicted by the linear approximation.

Parameters
• fit_intercept (bool, default=True) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

• normalize (bool, default=False) – This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.

• copy_X (bool, default=True) – If True, X will be copied; else, it may be overwritten.

• positive (bool, default=False) – When set to True, forces the coefficients to be positive. This option is only supported for dense arrays.

coef_

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

Type

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

rank_

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

Type

int

singular_

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

Type

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

intercept_

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

Type

float or array of shape (n_targets,)

n_features_in_

Number of features seen during fit.

Type

int

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.

__init__(*, fit_intercept=True, normalize=False, copy_X=True, positive=False)[source]

Methods

 __init__(*[, fit_intercept, normalize, ...]) fit(X, y[, sample_weight]) Fit linear model. 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 $$R^2$$ of the prediction. set_params(**params) Set the parameters of this estimator.