class_docstr: Base class for a Dynamic Classifier Selection (dcs) method.
  All dynamic classifier selection classes should inherit from this class.
  
  Warning: This class should not be used directly, use derived classes
  instead.
class_docstr_tok: ["Base","class","for","a","Dynamic","Classifier","Selection","(","dcs",")","method",".","All","dynamic","classifier","selection","classes","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=7,
      DFP=False,
      safe_k=None,
      with_IH=False,
      IH_rate=0.30,
      selection_method="best",
      diff_thresh=0.1,
      random_state=None,
      knn_classifier="knn",
      knn_metric="minkowski",
      DSEL_perc=0.5,
      knne=False,
      n_jobs=-1,
  ):
      super(BaseDCS, self).__init__(
          pool_classifiers=pool_classifiers,
          k=k,
          DFP=DFP,
          with_IH=with_IH,
          safe_k=safe_k,
          IH_rate=IH_rate,
          random_state=random_state,
          knn_classifier=knn_classifier,
          knn_metric=knn_metric,
          DSEL_perc=DSEL_perc,
          knne=knne,
          n_jobs=n_jobs,
          voting="single",
      )
  
      self.selection_method = selection_method
      self.diff_thresh = diff_thresh
  
file_path: DESlib/deslib/dcs/base.py
imports: from abc import ABCMeta
  from copy import copy
  import numpy
  from sklearn.utils.validation import check_random_state
  from deslib.base import BaseDS
  from deslib.util.aggregation import majority_voting_rule
label_desc: Use image node_id 1 to create a new BaseDCS object from inherited base classes: BaseDS with example: obj = BaseDCS(pool_classifiers, k, DFP, safe_k, with_IH, IH_rate, selection_method, diff_thresh, random_state, knn_classifier, knn_metric, DSEL_perc, knne, n_jobs)