code: def Q_statistic(y, y_pred1, y_pred2):
      """Calculates the Q-statistics diversity measure between a pair of
      classifiers. The Q value is in a range [-1, 1]. Classifiers that tend to
      classify the same object correctly will have positive values of Q, and
      Q = 0 for two independent classifiers.
  
      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
      -------
      Q : The q-statistic measure between two classifiers
      """
      N00, N10, N01, N11 = _process_predictions(y, y_pred1, y_pred2)
      Q = ((N11 * N00) - (N01 * N10)) / ((N11 * N00) + (N01 * N10))
      return Q
  
file_path: DESlib/deslib/util/diversity.py
imports: import sys
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: Q_statistic(y, y_pred1, y_pred2) and returns: Q