class_docstr: Method of Morris for analyzing blackbox systems.
  If using this please cite the package owners as can be found here: https://github.com/SALib/SALib
  
  Morris, Max D. "Factorial sampling plans for preliminary computational experiments."
  Technometrics 33.2 (1991): 161-174.
docstr: Provides approximate global explanation for blackbox model.
  
  Args:
      name: User-defined explanation name.
  
  Returns:
      An explanation object, visualizes dependence plots.
docstr_tok: ["Provides","approximate","global","explanation","for","blackbox","model",".","Args",":","name",":","User-defined","explanation","name",".","Returns",":","An","explanation","object",",","visualizes","dependence","plots","."]
code: def explain_global(self, name=None):
      """Provides approximate global explanation for blackbox model.
  
      Args:
          name: User-defined explanation name.
  
      Returns:
          An explanation object, visualizes dependence plots.
      """
  
      if name is None:
          name = gen_name_from_class(self)
  
      overall_data_dict = {
          "names": self.feature_names_in_,
          "scores": self.mu_star_,
          "convergence_index": self.convergence_index_,
      }
  
      specific_data_dicts = []
      for feat_idx, feature_name in enumerate(self.feature_names_in_):
          specific_data_dict = {
              "type": "morris",
              "mu": self.mu_[feat_idx],
              "mu_star": self.mu_star_[feat_idx],
              "sigma": self.sigma_[feat_idx],
              "mu_star_conf": self.mu_star_conf_[feat_idx],
          }
          specific_data_dicts.append(specific_data_dict)
  
      internal_obj = {
          "overall": overall_data_dict,
          "specific": specific_data_dicts,
      }
  
      global_selector = gen_global_selector(
          len(self.feature_names_in_),
          self.feature_names_in_,
          self.feature_types_in_,
          self.unique_val_counts_,
          self.mu_star_,
      )
  
      return MorrisExplanation(
          "global",
          internal_obj,
          feature_names=self.feature_names_in_,
          feature_types=self.feature_types_in_,
          name=name,
          selector=global_selector,
      )
  
imports: from ..api.base import ExplainerMixin
  from ..api.templates import FeatureValueExplanation
  from ..utils._explanation import gen_name_from_class, gen_global_selector
  from abc import ABC, abstractmethod
  import numpy
  from ..utils._clean_x import preclean_X
  from ..utils._unify_predict import determine_classes, unify_predict_fn
  from ..utils._unify_data import unify_data
label_desc: Use image node_id 2 for calling the MorrisSensitivity obj's underlying member method code with example usage: obj.explain_global(name) and returns: MorrisExplanation