http://scikit-learn.org/stable/modules/model_evaluation.html
3.3.1. The scoring parameter: defining model evaluation rules
Model selection and evaluation using tools, such as model_selection.GridSearchCV and model_selection.cross_val_score, take a scoring parameter that controls what metric they apply to the estimators evaluated.
3.3.1.1. Common cases: predefined values
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 |
Usage examples: