code: def binary_log_loss(y_true, y_prob):
      """Compute binary logistic loss for classification.
  
      This is identical to log_loss in binary classification case,
      but is kept for its use in multilabel case.
  
      Parameters
      ----------
      y_true : array-like or label indicator matrix
          Ground truth (correct) labels.
  
      y_prob : array-like of float, shape = (n_samples, n_classes)
          Predicted probabilities, as returned by a classifier's
          predict_proba method.
  
      Returns
      -------
      loss : float
          The degree to which the samples are correctly predicted.
      """
      y_prob = np.clip(y_prob, 1e-10, 1 - 1e-10)
  
      return (
          -np.sum(
              y_true * np.log(y_prob)
              + (1 - y_true) * np.log(1 - y_prob)
          )
          / y_prob.shape[0]
      )
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neural_network/_base.py
imports: import numpy
  from ..utils.fixes import expit
label_desc: Use image node_id 12 for calling a global function with example usage: binary_log_loss(y_true, y_prob) and returns: unknown