class_docstr: An ensemble of decision trees.
  
  This object provides a common interface to many different types of models.
class_docstr_tok: ["An","ensemble","of","decision","trees",".","This","object","provides","a","common","interface","to","many","different","types","of","models","."]
docstr: A consistent interface to make predictions from this model.
          
docstr_tok: ["A","consistent","interface","to","make","predictions","from","this","model","."]
code: def get_transform(self):
      """A consistent interface to make predictions from this model."""
      if self.model_output == "raw":
          transform = "identity"
      elif self.model_output in ("probability", "probability_doubled"):
          if self.tree_output == "log_odds":
              transform = "logistic"
          elif self.tree_output == "probability":
              transform = "identity"
          else:
              emsg = (
                  'model_output = "probability" is not yet supported when model.tree_output = '
                  f'"{self.tree_output}"!'
              )
              raise NotImplementedError(emsg)
      elif self.model_output == "log_loss":
          if self.objective == "squared_error":
              transform = "squared_loss"
          elif self.objective == "binary_crossentropy":
              transform = "logistic_nlogloss"
          else:
              emsg = (
                  'model_output = "log_loss" is not yet supported when model.objective = '
                  f'"{self.objective}"!'
              )
              raise NotImplementedError(emsg)
      else:
          emsg = (
              f"Unrecognized model_output parameter value: {str(self.model_output)}! "
              f"If `model.{str(self.model_output)}` is a valid function, open a Github issue to ask "
              "that this method be supported. If you want 'predict_proba' just use 'probability' for now."
          )
          raise ValueError(emsg)
  
      return transform
  
imports: import json
  import os
  import struct
  import time
  import warnings
  import numpy
  import pandas
  import scipy.sparse
  import scipy.special
  from packaging import version
  from ..None import maskers
  from .._explanation import Explanation
  from ..utils import assert_import, record_import_error, safe_isinstance
  from ..utils._exceptions import DimensionError, ExplainerError, InvalidFeaturePerturbationError, InvalidMaskerError, InvalidModelError
  from ..utils._legacy import DenseData
  from ._explainer import Explainer
label_desc: Use image node_id 2 for calling the TreeEnsemble obj's underlying member method code with example usage: obj.get_transform() and returns: transform