code: def log_loss(y_true, y_prob):
      """Compute Logistic loss for classification.
  
      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)
  
      if y_prob.shape[1] == 1:
          y_prob = np.append(1 - y_prob, y_prob, axis=1)
  
      if y_true.shape[1] == 1:
          y_true = np.append(1 - y_true, y_true, axis=1)
  
      return -np.sum(y_true * np.log(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 11 for calling a global function with example usage: log_loss(y_true, y_prob) and returns: unknown