class_docstr: Base class for iterative model fitting along a regularization path
class_docstr_tok: ["Base","class","for","iterative","model","fitting","along","a","regularization","path"]
code: def __init__(
      self,
      eps=1e-3,
      n_alphas=100,
      alphas=None,
      fit_intercept=True,
      normalize=False,
      precompute="auto",
      max_iter=1000,
      tol=1e-4,
      copy_X=True,
      cv=None,
      verbose=False,
      n_jobs=1,
      positive=False,
      random_state=None,
      selection="cyclic",
  ):
      self.eps = eps
      self.n_alphas = n_alphas
      self.alphas = alphas
      self.fit_intercept = fit_intercept
      self.normalize = normalize
      self.precompute = precompute
      self.max_iter = max_iter
      self.tol = tol
      self.copy_X = copy_X
      self.cv = cv
      self.verbose = verbose
      self.n_jobs = n_jobs
      self.positive = positive
      self.random_state = random_state
      self.selection = selection
  
imports: import sys
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import sparse
  from .base import LinearModel, _pre_fit
  from ..base import RegressorMixin
  from .base import _preprocess_data
  from ..utils import check_array, check_X_y, deprecated
  from ..utils.validation import check_random_state
  from ..model_selection import check_cv
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..externals.six.moves import xrange
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.validation import column_or_1d
  from ..exceptions import ConvergenceWarning
  from .None import cd_fast
label_desc: Use image node_id 1 to create a new LinearModelCV object from inherited base classes:  with example: obj = LinearModelCV(eps, n_alphas, alphas, fit_intercept, normalize, precompute, max_iter, tol, copy_X, cv, verbose, n_jobs, positive, random_state, selection)