length.isolation_forest

Returns the number of trees in an 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.

David Cortes

isotree

Isolation-Based Outlier Detection

Thibaut Goetghebuer-Planchon

David Blackman

Sebastiano Vigna

NumPy Developers

SciPy Developers

Enthought Inc

Stephen Moshier

length.isolation_forest function

<dl><dt>x</dt>
<dd>An isolation forest model, as returned by function isolation.forest.</dd></dl>

Arguments

Get Number of Trees in Model — length.isolation_forest

<dl>

<dt>x</dt>
<dd>An isolation forest model, as returned by function isolation.forest.</dd>

</dl>

length.isolation_forest: Get Number of Trees in Model

Description

Usage

Value

Arguments