Method evidence record
Isolation Forest
Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.
Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.
Source record
Recorded
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Isolation Forest (Anomaly Detection via Random Partitioning)
Taxonomic method recordml-modelmachine-learning
Open full method1 recorded citation, copied from the method source record.
Curated claims
Assessed
Claims persisted in the evidence ledger, each with its own assessment.
No curated claims yet
This view does not invent a claim assessment when the ledger has none.
Related methods
Suggested5
Generated from the method graph and shown as machine-suggested relations. No evidence claim is inferred.
- Same method familyDecision Tree
machine-suggestedRelational suggestion, not evidence.
- Same method familyGaussian Mixture Model
machine-suggestedRelational suggestion, not evidence.
- Same method familyPrincipal Component Analysis
machine-suggestedRelational suggestion, not evidence.
- Same method familyRandom Forest
machine-suggestedRelational suggestion, not evidence.
- Same method familyt-SNE
machine-suggestedRelational suggestion, not evidence.