class_docstr: LinearRegression is a simple machine learning model where the response y is
  modelled by a linear combination of the predictors in X.
  
  cuML's dask Linear Regression (multi-node multi-gpu) expects dask cuDF
  DataFrame and provides an algorithms, Eig, to fit a linear model.
  And provides an eigendecomposition-based algorithm to fit a linear model.
  (SVD, which is more stable than eig, will be added in an upcoming version.)
  Eig algorithm is usually preferred when the X is a tall and skinny matrix.
  As the number of features in X increases, the accuracy of Eig algorithm
  drops.
  
  This is an experimental implementation of dask Linear Regression. It
  supports input X that has more than one column. Single column input
  X will be supported after SVD algorithm is added in an upcoming version.
  
  Parameters
  ----------
  algorithm : 'eig'
      Eig uses a eigendecomposition of the covariance matrix, and is much
      faster.
      SVD is slower, but guaranteed to be stable.
  fit_intercept : boolean (default = True)
      LinearRegression adds an additional term c to correct for the global
      mean of y, modeling the response as "x * beta + c".
      If False, the model expects that you have centered the data.
  normalize : boolean (default = False)
      If True, the predictors in X will be normalized by dividing by its
      L2 norm.
      If False, no scaling will be done.
  
  Attributes
  ----------
  coef_ : cuDF series, shape (n_features)
      The estimated coefficients for the linear regression model.
  intercept_ : array
      The independent term. If `fit_intercept` is False, will be 0.
code: def __init__(self, *, client=None, verbose=False, **kwargs):
      super().__init__(client=client, verbose=verbose, **kwargs)
  
file_path: cuml/python/cuml/dask/linear_model/linear_regression.py
imports: from cuml.dask.common.base import BaseEstimator
  from cuml.dask.common.base import DelayedPredictionMixin
  from cuml.dask.common.base import mnmg_import
  from cuml.dask.common.base import SyncFitMixinLinearModel
  from raft_dask.common.comms import get_raft_comm_state
  from dask.distributed import get_worker
label_desc: Use image node_id 1 to create a new LinearRegression object from inherited base classes: BaseEstimator, SyncFitMixinLinearModel, DelayedPredictionMixin with example: obj = LinearRegression()