The Anomaly Detection handler implements supervised, semi-supervised, and unsupervised anomaly detection algorithms using the pyod, catboost, xgboost, and sklearn libraries. The models were chosen based on the results in the following benchmark paper.
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 called outlier
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.
To run example queries, use the data from this CSV file.
https://www.loom.com/share/0996e5faa3f7415bacd51a6e8e161d5e?sid=9bacd29a-975b-4a94-b081-de2255b93607
https://www.loom.com/share/c22335d83cb04ac281e2ef080792f2dd
The Anomaly Detection handler implements supervised, semi-supervised, and unsupervised anomaly detection algorithms using the pyod, catboost, xgboost, and sklearn libraries. The models were chosen based on the results in the following benchmark paper.
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 called outlier
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.
To run example queries, use the data from this CSV file.
https://www.loom.com/share/0996e5faa3f7415bacd51a6e8e161d5e?sid=9bacd29a-975b-4a94-b081-de2255b93607
https://www.loom.com/share/c22335d83cb04ac281e2ef080792f2dd