Binary Classification
If your predictive query is determining one of two values (e.g., true/false, yes/no), Kumo provides the following evaluation metrics:- Accuracy
- Area Under The Receiver Operating Characteristic Curve (AUROC)
- Area Under The Precision-Recall Curve (AUPRC)
- Confusion Matrix
- Gain Chart
Multiclass Classification
If your predictive query is distinguishing between three or more values (e.g., theLAST
value from a high cardinality categorical column in the target table), Kumo provides the following evaluation metrics:
- Accuracy
Multilabel Classification
If your predictive query returns one or more values from a list of categorical value candidates (e.g., theLIST_DISTINCT
of values from a high cardinality categorical column in the target table), Kumo provides the following macro/micro/per-label versions of the following evaluation metrics:
- AUPRC
- AUROC
- AP
LIST_DISTINCT
over a foreign key column, it will trigger a multilabel classification task.
Link Prediction
If your pQuery returns a list of distinct values associated with your entity (e.g.LIST_DISTINCT
), Kumo provides top-k retrieval metrics (for K = 1, 10, and 100):
- F1@K
- MAP@K
- Precision@K
- Recall@K
LIST_DISTINCT
over a foreign key column; otherwise it will trigger a multilabel classification task.
Regression
If your pQuery returns a numeric value (e.g. theSUM/MAX
/etc. of values from a numerical column in the target table), Kumo provides the following metrics:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Square Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- Symmetric Mean Absolute Percentage Error (SMAPE)
- Distribution of Predictions Histogram