class_docstr: Confusion Matrix for binary and multi-class classification.
  
  Parameters
  ----------
  classes
      The initial set of classes. This is optional and serves only for displaying purposes.
  
  Examples
  --------
  
  >>> from river import metrics
  
  >>> y_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']
  >>> y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']
  
  >>> cm = metrics.ConfusionMatrix()
  
  >>> for yt, yp in zip(y_true, y_pred):
  ...     cm.update(yt, yp)
  
  >>> cm
         ant  bird   cat
   ant     2     0     0
  bird     0     0     1
   cat     1     0     2
  
  >>> cm['bird']['cat']
  1.0
  
  Notes
  -----
  This confusion matrix is a 2D matrix of shape `(n_classes, n_classes)`, corresponding
  to a single-target (binary and multi-class) classification task.
  
  Each row represents `true` (actual) class-labels, while each column corresponds
  to the `predicted` class-labels. For example, an entry in position `[1, 2]` means
  that the true class-label is 1, and the predicted class-label is 2 (incorrect prediction).
  
  This structure is used to keep updated statistics about a single-output classifier's
  performance and to compute multiple evaluation metrics.
code: def false_positives(self, label):
      return self.sum_col[label] - self.data[label][label]
  
imports: from __future__ import annotations
  import functools
  from collections import defaultdict
  from river import metrics, utils
label_desc: Use image node_id 11 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.false_positives(label) and returns: unknown