Isolation-Based Outlier Detection
Create Isolation Forest Model
Add additional (single) tree to isolation forest model
Append isolation trees from one model into another
Build Indexer for Faster Terminal Node Predictions and/or Distance Cal...
Deep-Copy an Isolation Forest Model Object
Drop Imputer Sub-Object from Isolation Forest Model Object
Drop Indexer Sub-Object from Isolation Forest Model Object
Drop Reference Points from Isolation Forest Model Object
Export Isolation Forest model
Get Number of Nodes per Tree
Load an Isolation Forest model exported from Python
Check if two Isolation Forest Models Share the Same C++ Object
Plot Tree from Isolation Forest Model
Unpack isolation forest model after de-serializing
Set Number of Threads for Isolation Forest Model Object
Set Reference Points to Calculate Distances or Kernels With
Subset trees of a given model
Generate GraphViz Dot Representation of Tree
Generate JSON representations of model trees
Generate SQL statements from Isolation Forest model
Get Number of Trees in Model
Predict method for Isolation Forest
Print summary information from Isolation Forest model
Print summary information from Isolation Forest model
Get Variable Names for Isolation Forest Model
Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <arXiv:2110:13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <arXiv:1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <arXiv:2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.