Additional information
-
If no labelled data, we use an unsupervised learner with the syntax
CREATE ANOMALY DETECTION MODEL <model_name>
without specifying the target to predict. MindsDB then adds a column calledoutlier
when generating results. - If we have labelled data, we use the regular model creation syntax. There is backend logic that chooses between a semi-supervised algorithm (currently XGBOD) vs. a supervised algorithm (currently CatBoost).
- If multiple models are provided, then we create an ensemble and use majority voting.
- See the anomaly detection proposal document for more information.