class_docstr: Implementation of the scikit-learn API for LightGBM.
class_docstr_tok: ["Implementation","of","the","scikit-learn","API","for","LightGBM","."]
docstr: :obj:`str` or :obj:`callable`: The concrete objective used while fitting this model.
code: def objective_(
      self,
  ) -> Union[str, _LGBM_ScikitCustomObjectiveFunction]:
      """:obj:`str` or :obj:`callable`: The concrete objective used while fitting this model."""
      if not self.__sklearn_is_fitted__():
          raise LGBMNotFittedError(
              "No objective found. Need to call fit beforehand."
          )
      return self._objective
  
imports: import copy
  from inspect import signature
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  from .basic import Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _LGBM_BoosterBestScoreType, _LGBM_CategoricalFeatureConfiguration, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration, _LGBM_GroupType, _LGBM_InitScoreType, _LGBM_LabelType, _LGBM_WeightType, _log_warning
  from .callback import _EvalResultDict, record_evaluation
  from .compat import SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray, _LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase, _LGBMComputeSampleWeight, _LGBMCpuCount, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, dt_DataTable, np_random_Generator, pd_DataFrame
  from .engine import train
label_desc: Use image node_id 14 for calling the LGBMModel obj's underlying member method code with example usage: obj.objective_() and returns: self