Train model (Anomaly)
Last updated
Last updated
Take the output from a DSP block and train an anomaly detection model using K-means or GMM. Updates are streamed over the websocket API.
Project ID
Learn Block ID, use the impulse functions to retrieve the ID
Which axes (indexes from DSP script) to include in the training set
Number of clusters for K-means, or number of components for GMM
32
Minimum confidence rating required before tagging as anomaly
0.3
If set, skips creating embeddings and measuring memory (used in tests)
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Job identifier. Status updates will include this identifier.
12873488112