summary.xrf

Summarize an eXtreme RuleFit model

An implementation of the RuleFit algorithm as described in Friedman & Popescu
(2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used
to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result
is a model that learns similarly to a tree ensemble, while often offering improved interpretability
and achieving improved scoring runtime in live applications. Several algorithms for
reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to
several million rows and support sparse representations to handle tens of thousands of dimensions.

Karl Holub

eXtreme RuleFit

summary.xrf function

<dl><dt>object</dt>
<dd>an object of class "xrf"</dd>
<dt>...</dt>
<dd>ignored arguments</dd></dl>

Arguments

Summarize an eXtreme RuleFit model — summary.xrf

<dl>

<dt>object</dt>
<dd>an object of class "xrf"</dd>


<dt>...</dt>
<dd>ignored arguments</dd>

</dl>

summary.xrf: Summarize an eXtreme RuleFit model

Description

Usage

Arguments

Examples