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.
  
  Parameters
  ----------
  tree_limit : None (default) or int
      Limit the number of trees used by the model. By default None means no use the limit of the
      original model, and -1 means no limit.
docstr_tok: ["A","consistent","interface","to","make","predictions","from","this","model",".","Parameters","--","--","--","--","--","tree_limit",":","None","(","default",")","or","int","Limit","the","number","of","trees","used","by","the","model",".","By","default","None","means","no","use","the","limit","of","the","original","model",",","and","-1","means","no","limit","."]
code: def predict(self, X, y=None, output=None, tree_limit=None):
      """A consistent interface to make predictions from this model.
  
      Parameters
      ----------
      tree_limit : None (default) or int
          Limit the number of trees used by the model. By default None means no use the limit of the
          original model, and -1 means no limit.
      """
  
      if output is None:
          output = self.model_output
  
      if self.model_type == "pyspark":
          # import pyspark
          # TODO: support predict for pyspark
          raise NotImplementedError(
              "Predict with pyspark isn't implemented. Don't run 'interventional' as feature_perturbation."
          )
  
      # see if we have a default tree_limit in place.
      if tree_limit is None:
          tree_limit = (
              -1 if self.tree_limit is None else self.tree_limit
          )
  
      # convert dataframes
      if isinstance(X, (pd.Series, pd.DataFrame)):
          X = X.values
      flat_output = False
      if len(X.shape) == 1:
          flat_output = True
          X = X.reshape(1, X.shape[0])
      if X.dtype.type != self.input_dtype:
          X = X.astype(self.input_dtype)
      X_missing = np.isnan(X, dtype=bool)
      assert isinstance(X, np.ndarray), "Unknown instance type: " + str(
          type(X)
      )
      assert (
          len(X.shape) == 2
      ), "Passed input data matrix X must have 1 or 2 dimensions!"
  
      if tree_limit < 0 or tree_limit > self.values.shape[0]:
          tree_limit = self.values.shape[0]
  
      if output == "logloss":
          assert (
              y is not None
          ), "Both samples and labels must be provided when explaining the loss (i.e. `explainer.shap_values(X, y)`)!"
          assert X.shape[0] == len(y), (
              "The number of labels (%d) does not match the number of samples to explain (%d)!"
              % (len(y), X.shape[0])
          )
      transform = self.get_transform()
      assert_import("cext")
      output = np.zeros((X.shape[0], self.num_outputs))
      _cext.dense_tree_predict(
          self.children_left,
          self.children_right,
          self.children_default,
          self.features,
          self.thresholds,
          self.values,
          self.max_depth,
          tree_limit,
          self.base_offset,
          output_transform_codes[transform],
          X,
          X_missing,
          y,
          output,
      )
  
      # drop dimensions we don't need
      if flat_output:
          if self.num_outputs == 1:
              return output.flatten()[0]
          else:
              return output.reshape(-1, self.num_outputs)
      else:
          if self.num_outputs == 1:
              return output.flatten()
          else:
              return output
  
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 3 for calling the TreeEnsemble obj's underlying member method code with example usage: obj.predict(X, y, output, tree_limit) and returns: output, output, output, output