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Baseline Evaluation Metrics

On your predictive query's evaluation page, Kumo provides comparisons of your model versus some heuristic baselines for temporal binary classification, link prediction, and regression tasks. This allows you to compare the results of your Kumo model to a range of baselines for evaluating the efficacy of your model.

Baselines are computed on a per-entity level (i.e., by using the past label as a forecast for regression, or by recommending past user behavior for link prediction).

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Note: Baselines are not supported when timeframe_step is None.

Baselines Per Task Type

The following are the current baselines supported by Kumo, organized prediction task type:

Task TypeTemporal Problem
Link PredictionPer Entity Frequency: Predicting each entities with the most popular ranked list of target values for this entity in all previous splits
Regression/Logistic RegressionPer Entity Mean: (personalized average) Predicting each entity with the average of all target values for this entity in all previous splits
Per Entity last 1 label: Predicting each entity with the target value in the previous time frames
Binary ClassificationPer Entity Mean: Predicting each entities with the average of all target values for this entity in all previous splits
Per Entity last 1 label: Predicting each entity with the target value in the previous time frame