GridSearchCV scoring 参考

http://scikit-learn.org/stable/modules/model_evaluation.html

• Scoring parameter: Model-evaluation tools using cross-validation (such as model_selection.cross_val_score andmodel_selection.GridSearchCV) rely on an internal scoring strategy. This is discussed in the section The scoring parameter: defining model evaluation rules.

• For the most common use cases, you can designate a scorer object with the scoring parameter; the table below shows all possible values. All scorer objects follow the convention that higher return values are better than lower return values. Thus metrics which measure the distance between the model and the data, like metrics.mean_squared_error, are available as neg_mean_squared_error which return the negated value of the metric.

  Scoring	Function	Comment
  Classification
  ‘accuracy’	metrics.accuracy_score
  ‘average_precision’	metrics.average_precision_score
  ‘f1’	metrics.f1_score	for binary targets
  ‘f1_micro’	metrics.f1_score	micro-averaged
  ‘f1_macro’	metrics.f1_score	macro-averaged
  ‘f1_weighted’	metrics.f1_score	weighted average
  ‘f1_samples’	metrics.f1_score	by multilabel sample
  ‘neg_log_loss’	metrics.log_loss	requires predict_proba support
  ‘precision’ etc.	metrics.precision_score	suffixes apply as with ‘f1’
  ‘recall’ etc.	metrics.recall_score	suffixes apply as with ‘f1’
  ‘roc_auc’	metrics.roc_auc_score
  Clustering
  ‘adjusted_rand_score’	metrics.adjusted_rand_score
  Regression
  ‘neg_mean_absolute_error’	metrics.mean_absolute_error
  ‘neg_mean_squared_error’	metrics.mean_squared_error
  ‘neg_median_absolute_error’	metrics.median_absolute_error
  ‘r2’	metrics.r2_score