Performance metrics

Performance metrics are measurements that quantitatively calculate your model’s performance. The metrics consider what the model predicted (prediction) against what actually happens (label).

The metrics supported by Superwise include: RMSE, MSE, MAE, MAPE, Accuracy, Recall, Precision, F1, Log Loss and ROC AUC.
These scores are calculated from the time the metric was created, and not historically.

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Pro tip

Configure your model's performance metrics as soon as you connect it to Superwise.

You can use these metrics, depending on the type of your label and prediction, as follows:

Prediction type

Label type

Possible metrics

Boolean

Boolean

Accuracy, Error-rate, Recall, Precision, F1

Categorical

Categorical

Accuracy, Error-rate, Recall, Precision, F1

Boolean

Categorical

Accuracy,Error-rate, Recall, Precision, F1

Categorical

Boolean

Accuracy, Error-rate, Recall, Precision, F1

Numeric

Numeric

RMSE, MSE, MAE, MAPE

Numeric

Boolean

Log Loss, ROC AUC

Numeric

Categorical

Log Loss, ROC AUC

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Read more

For more information about how to configure performance metrics: Configure performance metrics


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