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

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.

__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.