class_docstr: Interpretation methods for classification models.
docstr: Sorted descending largest non-diagonal entries of confusion matrix (actual, predicted, # occurrences
docstr_tok: ["Sorted","descending","largest","non-diagonal","entries","of","confusion","matrix","(","actual",",","predicted",",","#","occurrences"]
code: def most_confused(self, min_val=1):
      "Sorted descending largest non-diagonal entries of confusion matrix (actual, predicted, # occurrences"
      cm = self.confusion_matrix()
      np.fill_diagonal(cm, 0)
      res = [
          (self.vocab[i], self.vocab[j], cm[i, j])
          for i, j in zip(*np.where(cm >= min_val))
      ]
      return sorted(res, key=itemgetter(2), reverse=True)
  
imports: from __future__ import annotations
  from .data.all import *
  from .optimizer import *
  from .learner import *
  from .tabular.core import *
  import sklearn.metrics
label_desc: Use image node_id 4 for calling the ClassificationInterpretation obj's underlying member method code with example usage: obj.most_confused(min_val) and returns: sorted