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 3 months ago