class_docstr: Estimator for learning linear regressors by (block) coordinate descent.
  
  The objective functions considered take the form
  
  minimize F(W) = C * L(W) + alpha * R(W),
  
  where L(W) is a loss term and R(W) is a penalty term.
  
  Parameters
  ----------
  loss : str, 'squared'
      The loss function to be used.
  
  penalty : str, 'l2', 'l1', 'l1/l2'
      The penalty to be used.
  
      - l2: ridge
      - l1: lasso
      - l1/l2: group lasso
  
  For other parameters, see `CDClassifier`.
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code: def __init__(
      self,
      C=1.0,
      alpha=1.0,
      loss="squared",
      penalty="l2",
      max_iter=50,
      tol=1e-3,
      termination="violation_sum",
      shrinking=True,
      max_steps=30,
      sigma=0.01,
      beta=0.5,
      warm_start=False,
      debiasing=False,
      Cd=1.0,
      warm_debiasing=False,
      selection="cyclic",
      permute=True,
      callback=None,
      n_calls=100,
      random_state=None,
      verbose=0,
      n_jobs=1,
  ):
      self.C = C
      self.alpha = alpha
      self.loss = loss
      self.penalty = penalty
      self.max_iter = max_iter
      self.tol = tol
      self.termination = termination
      self.shrinking = shrinking
      self.max_steps = max_steps
      self.sigma = sigma
      self.beta = beta
      self.warm_start = warm_start
      self.debiasing = debiasing
      self.Cd = Cd
      self.warm_debiasing = warm_debiasing
      self.selection = selection
      self.permute = permute
      self.callback = callback
      self.n_calls = n_calls
      self.random_state = random_state
      self.verbose = verbose
      self.coef_ = None
      self.violation_init_ = {}
      self.n_jobs = n_jobs
  
imports: import numpy
  from joblib import Parallel, delayed
  from .base import BaseClassifier
  from .base import BaseRegressor
  from .dataset_fast import get_dataset
  from .primal_cd_fast import _primal_cd
  from .primal_cd_fast import Squared
  from .primal_cd_fast import SmoothHinge
  from .primal_cd_fast import SquaredHinge
  from .primal_cd_fast import ModifiedHuber
  from .primal_cd_fast import Log
label_desc: Use image node_id 1 to create a new CDRegressor object from inherited base classes: _BaseCD, BaseRegressor with example: obj = CDRegressor(C, alpha, loss, penalty, max_iter, tol, termination, shrinking, max_steps, sigma, beta, warm_start, debiasing, Cd, warm_debiasing, selection, permute, callback, n_calls, random_state, verbose, n_jobs)