class_docstr: 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
      :class:`~sklearn.preprocessing.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.
  
  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), 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.
  
  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 if
      `fit_intercept = False`.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
  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.
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code: def __init__(
      self,
      *,
      fit_intercept=True,
      normalize=False,
      copy_X=True,
      positive=False,
  ):
      self.fit_intercept = fit_intercept
      self.normalize = normalize
      self.copy_X = copy_X
      self.positive = positive
  
imports: import numbers
  from abc import ABCMeta, abstractmethod
  from numpy.linalg import LinAlgError
  from sklearn.utils.validation import check_is_fitted, _deprecate_positional_args
  from sklearn.base import MultiOutputMixin
  from ...None import execute
  from ...None import tensor
  from ...tensor.datasource import tensor
  from ..base import BaseEstimator, RegressorMixin, ClassifierMixin
  from ..preprocessing import normalize
  from ..utils.validation import _check_sample_weight, check_array, FLOAT_DTYPES
label_desc: Use image node_id 1 to create a new LinearRegression object from inherited base classes: MultiOutputMixin, RegressorMixin, LinearModel with example: obj = LinearRegression()