S3 method for building an "eXtreme RuleFit" model.
See <code>xrf.formula</code> for preferred entry point

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

xrf function

<dl><dt>object</dt>
<dd>an object describing the model to be fit</dd>
<dt>...</dt>
<dd>additional arguments</dd></dl>

Arguments

Fit an eXtreme RuleFit model — xrf

<dl>

<dt>object</dt>
<dd>an object describing the model to be fit</dd>


<dt>...</dt>
<dd>additional arguments</dd>

</dl>

xrf: Fit an eXtreme RuleFit model

Description

Usage

Arguments

Examples