Interpreting anomaly detection results
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- machine-learning
- Domain
- documentation, machine-learning
Research direction
Start with the linked results tutorial and the linked pages on influencers, multi-bucket anomalies, and ML results. Consolidate the requested material into a reusable procedure covering viewers, result types, scores and probabilities, actual and typical values, influencers, and multi-bucket anomalies.
Written by the indexing model from the issue text.
Description
Our content about how to interpret anomaly detection job is currently spread about and it would be useful to see if it can be improved by pulling it together into a procedure that (ideally) could then be re-used in whole or in part in multiple solutions.
It should include:
- Differences between Anomaly Explorer and Single Metric Viewer (covered at high level in tutorial)
- Information about what you can glean from influencers
- Interpreting multi-bucket anomalies
- A summary of the different types of results (e.g. model plot results, influencer results, bucket results, record results
- Interpreting anomaly scores and how they are calculated and how they differ from probability.(covered at high level in tutorial)
- Meaning of "actual" and "typical" values (covered at high level in tutorial)
- Dominant language
- Java
- Stars
- 105
- Forks
- 249
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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