class_docstr: This is an abstract class.
  All algorithms inherit from BaseAlgorithm.
code: def interpret(
      self,
      X_train,
      y_train,
      X_validation,
      y_validation,
      model_file_path,
      learner_name,
      target_name=None,
      class_names=None,
      metric_name=None,
      ml_task=None,
      explain_level=2,
  ):
      # do not produce feature importance for Baseline
      if self.algorithm_short_name == "Baseline":
          return
      if explain_level > 0:
          PermutationImportance.compute_and_plot(
              self,
              X_validation,
              y_validation,
              model_file_path,
              learner_name,
              metric_name,
              ml_task,
              self.params.get("n_jobs", -1),
          )
      if explain_level > 1:
          PlotSHAP.compute(
              self,
              X_train,
              y_train,
              X_validation,
              y_validation,
              model_file_path,
              learner_name,
              class_names,
              ml_task,
          )
  
imports: import uuid
  import numpy
  from supervised.utils.common import construct_learner_name
  from supervised.utils.importance import PermutationImportance
  from supervised.utils.shap import PlotSHAP
label_desc: Use image node_id 13 for calling the BaseAlgorithm obj's underlying member method code with example usage: obj.interpret(X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level) without return types