FLAML
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support anomaly detection
Add a task type 'anomaly_detection', and at least one learner for it.
From my understanding there are multiple types of anomalies - point anomalies, contextual anomalies, and collective anomalies - and also different types of anomaly detection algorithms - nearest-neighbor, clustering, classification, and statistic based.
If anyone has any suggestions or requests for this task, please comment.