Metrics¶
In our library you should use metrics inherited from Base Class. We have already made some wrappers around Sklearn Metrics.
Base Class¶
-
class
modelgym.metrics.
Metric
(scoring_function, requires_proba=False, is_min_optimal=False, name='default_name')¶ Metric class is a wrapper around sklearn.metrics class, with additional information: when optimizing this metric, should we minimize it (like log_loss) or maximize (like accuracy), and whether it’s calculation requires computed probabilities (like roc_auc).
Of course, not only sklearn.metrics could be wrapped into this class
Parameters: - scoring_function (types.FunctionType) – wrapped scoring function
- requires_proba (bool) – whether calculation of metric requires computed probabilities
- is_min_optimal (bool) – is the less the better
- name (str) – name of metric
Sklearn Metrics¶
-
class
modelgym.metrics.
Accuracy
(name='accuracy')¶ Bases:
modelgym.metrics.Metric
-
class
modelgym.metrics.
F1
(name='f1_score')¶ Bases:
modelgym.metrics.Metric
-
class
modelgym.metrics.
Logloss
(name='logloss')¶ Bases:
modelgym.metrics.Metric
-
class
modelgym.metrics.
Mse
(name='mse')¶ Bases:
modelgym.metrics.Metric
-
class
modelgym.metrics.
Precision
(name='precision')¶ Bases:
modelgym.metrics.Metric
-
class
modelgym.metrics.
Recall
(name='recall')¶ Bases:
modelgym.metrics.Metric
-
class
modelgym.metrics.
RocAuc
(name='roc_auc')¶ Bases:
modelgym.metrics.Metric