mars.learn.linear_model.LinearRegression#
- class mars.learn.linear_model.LinearRegression(*, fit_intercept=True, normalize=False, copy_X=True, positive=False)[源代码]#
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.
- 参数
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 useStandardScaler
before callingfit
on an estimator withnormalize=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)
- 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,)
参见
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.
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.