code: def agreement_measure(y, y_pred1, y_pred2):
      """Calculates the agreement measure between a pair of classifiers. This
      measure is calculated by the frequency that both classifiers either
      obtained the correct or incorrect prediction for any given sample
  
      Parameters
      ----------
      y : array of shape (n_samples):
          class labels of each sample.
  
      y_pred1 : array of shape (n_samples):
                predicted class labels by the classifier 1 for each sample.
  
      y_pred2 : array of shape (n_samples):
                predicted class labels by the classifier 2 for each sample.
  
      Returns
      -------
      agreement : The frequency at which both classifiers agrees
      """
      N00, _, _, N11 = _process_predictions(y, y_pred1, y_pred2)
      agreement = N00 + N11
      return agreement
  
file_path: DESlib/deslib/util/diversity.py
imports: import sys
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: agreement_measure(y, y_pred1, y_pred2) and returns: agreement