isotree v0.1.28


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Isolation-Based Outlier Detection

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>), and fair-cut forest (Cortes (2019) <arXiv:1911.06646>), for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>), and imputation of missing values (Cortes (2019) <arXiv:1911.06646>), based on random or guided decision tree splitting. 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.

Functions in isotree

Name Description
export.isotree.model Export Isolation Forest model
deepcopy.isotree Deep-Copy an Isolation Forest Model Object
predict.isolation_forest Predict method for Isolation Forest
print.isolation_forest Print summary information from Isolation Forest model
get.num.nodes Get Number of Nodes per Tree Generate SQL statements from Isolation Forest model
add.isolation.tree Add additional (single) tree to isolation forest model
load.isotree.model Load an Isolation Forest model exported from Python
append.trees Append isolation trees from one model into another
summary.isolation_forest Print summary information from Isolation Forest model
unpack.isolation.forest Unpack isolation forest model after de-serializing
isolation.forest Create Isolation Forest Model
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Last month downloads


Type Package
Date 2021-01-13
License BSD_2_clause + file LICENSE
LinkingTo Rcpp, Rcereal
RoxygenNote 7.1.1
NeedsCompilation yes
Packaged 2021-01-13 19:46:43 UTC; david
Repository CRAN
Date/Publication 2021-01-13 20:20:02 UTC

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