docstr: Compute the prediction accuracy
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  y : array-like, shape = [n_samples]
      Target values (true class labels).
  
  Returns
  ---------
  acc : float
      The prediction accuracy as a float
      between 0.0 and 1.0 (perfect score).
code: def score(self, X, y):
      """Compute the prediction accuracy
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
      y : array-like, shape = [n_samples]
          Target values (true class labels).
  
      Returns
      ---------
      acc : float
          The prediction accuracy as a float
          between 0.0 and 1.0 (perfect score).
  
      """
      y_pred = self.predict(X)
      acc = np.sum(y == y_pred, axis=0) / float(X.shape[0])
      return acc
  
file_path: mlxtend/mlxtend/_base/_classifier.py
imports: from time import time
  import numpy
label_desc: Use image node_id 3 for calling the _Classifier obj's underlying member method code with example usage: obj.score(X, y) and returns: acc