Metrics
In this article we provide a list of predefined metrics that can be used during model training and testing.
Metrics can be passed by their name via metrics
parameter in TrainingParams
and TestingParams
. To read more, visit Fine-tuning training parameters and Customizing testing metrics.
Metric name | Task | Description |
---|---|---|
MultipleTargetsRecall | Multiclass Classification | A fraction of correct class predictions. The result is one number. |
MultipleTargetsRecallPerClass | Multiclass Classification | The number of correctly predicted positive instances divided by the total number of actual positive instances for a given class. This yields one value (recall) per class. |
PrecisionAtK | Recommendations | The fraction of the top K predicted items that are relevant, computed for each instance and averaged over the dataset. |
MeanAveragePrecisionAtK | Recommendations | The average precision at each position of relevant items within the top K results, giving higher scores when relevant items appear earlier in the ranking, and then averages this over all instances. |
HitRateAtK | Recommendations | The fraction of cases where the top K predictions contain at least one relevant item. |
Updated 9 days ago