class_docstr: Uses Tree SHAP algorithms to explain the output of ensemble tree models.
  
  Tree SHAP is a fast and exact method to estimate SHAP values for tree models and ensembles of trees,
  under several different possible assumptions about feature dependence. It depends on fast C++
  implementations either inside an external model package or in the local compiled C extension.
class_docstr_tok: ["Uses","Tree","SHAP","algorithms","to","explain","the","output","of","ensemble","tree","models",".","Tree","SHAP","is","a","fast","and","exact","method","to","estimate","SHAP","values","for","tree","models","and","ensembles","of","trees",",","under","several","different","possible","assumptions","about","feature","dependence",".","It","depends","on","fast","C++","implementations","either","inside","an","external","model","package","or","in","the","local","compiled","C","extension","."]
docstr: This computes the expected value conditioned on the given label value.
          
docstr_tok: ["This","computes","the","expected","value","conditioned","on","the","given","label","value","."]
code: def __dynamic_expected_value(self, y):
      """This computes the expected value conditioned on the given label value."""
  
      return self.model.predict(
          self.data, np.ones(self.data.shape[0]) * y
      ).mean(0)
  
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 TreeExplainer obj's underlying member method code with example usage: obj.__dynamic_expected_value(y) and returns: self