Monitoring Model Quality

If you have a process for generating Ground Truth Labels for your predictions, you can ingest those labels in DMM to measure and monitor model prediction quality. DMM uses the row identifiers in the prediction and ground truth data to match the predicted and expected value. Based on those matches, it calculates the different model quality metrics.

For correct ingestion of Ground Truth Label, it is important to map the ground truth label column from the Ground Truth file to its corresponding prediction data column in the prediction dataset. When using the Guided Flow, Step 2 will require you to declare this mapping.

When you apply a date filter, DMM will use the timestamp values in the prediction data to filter with. It then matches the filtered predictions with the ground truth labels ingested (matches only labels ingested in last 90 days) and calculates the metrics for the matched predictions.

Following prediction quality metrics are supported for Classification models:

  1. Accuracy

  2. Precision

  3. Recall

  4. F1

  5. AUC ROC

  6. Log Loss

  7. Gini (Normalized)

You can view the Confusion Matrix and Classification Report for the data in the selected time range in the Charts section below the metrics table.

Note 1: DMM uses the ‘Weighted’ method of calculating these metrics.

Note 2: AUC ROC, Log Loss, Gini Norm are calculated only if Prediction Probability column type is declared as part of schema.

Note 3: Sample Weights is used in calculations for only the Gini Norm metric.

Following prediction quality metrics are supported for Regression models:

  1. Mean Square Error (MSE)

  2. Mean Absolute Error (MAE)

  3. Mean Absolute Percentage Error (MAPE)

  4. R-Squared (R2)