class_docstr: Base class for a DS method based on the potential function model.
  All DS methods based on the Potential function should inherit from this
  class.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
code: def __init__(
      self,
      pool_classifiers=None,
      k=None,
      DFP=False,
      with_IH=False,
      safe_k=None,
      IH_rate=0.30,
      mode="selection",
      voting="hard",
      selection_threshold=None,
      random_state=None,
      knn_classifier="knn",
      knn_metric="minkowski",
      DSEL_perc=0.5,
      n_jobs=-1,
  ):
      super(BaseProbabilistic, self).__init__(
          pool_classifiers=pool_classifiers,
          k=k,
          DFP=DFP,
          with_IH=with_IH,
          safe_k=safe_k,
          IH_rate=IH_rate,
          mode=mode,
          random_state=random_state,
          knn_classifier=knn_classifier,
          knn_metric=knn_metric,
          DSEL_perc=DSEL_perc,
          n_jobs=n_jobs,
          voting=voting,
      )
  
      self.selection_threshold = selection_threshold
  
file_path: DESlib/deslib/des/probabilistic/base.py
imports: from abc import abstractmethod, ABCMeta
  import numpy
  from deslib.des.base import BaseDES
label_desc: Use image node_id 1 to create a new BaseProbabilistic object from inherited base classes: BaseDES with example: obj = BaseProbabilistic(pool_classifiers, k, DFP, with_IH, safe_k, IH_rate, mode, voting, selection_threshold, random_state, knn_classifier, knn_metric, DSEL_perc, n_jobs)