class_docstr: Wrapper class that wraps GBDT models as DeepChem models.
  
  This class supports LightGBM/XGBoost models.
class_docstr_tok: ["Wrapper","class","that","wraps","GBDT","models","as","DeepChem","models",".","This","class","supports","LightGBM\/XGBoost","models","."]
code: def _check_model_type(self) -> str:
      class_name = self.model.__class__.__name__
      if class_name.endswith("Classifier"):
          return "classification"
      elif class_name.endswith("Regressor"):
          return "regression"
      elif class_name == "NoneType":
          return "none"
      else:
          raise ValueError(
              "{} is not a supported model instance.".format(class_name)
          )
  
file_path: deepchem/deepchem/models/gbdt_models/gbdt_model.py
imports: import os
  import logging
  import tempfile
  import warnings
  from typing import Callable, Optional, Union
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.model_selection import train_test_split
  from deepchem.data import Dataset
  from deepchem.models.sklearn_models import SklearnModel
label_desc: Use image node_id 2 for calling the GBDTModel obj's underlying member method code with example usage: obj._check_model_type() and returns: str, str, str