class_docstr: Logistic Regression oneDAL implementation.
class_docstr_tok: ["Logistic","Regression","oneDAL","implementation","."]
code: def __init__(
      self,
      tol=1e-4,
      C=1.0,
      fit_intercept=True,
      solver="newton-cg",
      max_iter=100,
      *,
      algorithm="dense_batch",
      **kwargs,
  ):
      super().__init__(
          tol=tol,
          C=C,
          fit_intercept=fit_intercept,
          solver=solver,
          max_iter=max_iter,
          algorithm=algorithm,
      )
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Number
  import numpy
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import get_dtype, make2d
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin
  from ..common._policy import _get_policy
  from ..datatypes import _convert_to_supported, from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _num_features, _type_of_target
label_desc: Use image node_id 1 to create a new LogisticRegression object from inherited base classes: ClassifierMixin, BaseLogisticRegression with example: obj = LogisticRegression(tol, C, fit_intercept, solver, max_iter)