sym.rf

Symbolic Regression with Random Forest

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

Oldemar Rodriguez

RSDA

R to Symbolic Data Analysis

Jose Emmanuel Chacon

Carlos Aguero

Jorge Arce

sym.rf function

<dl><dt>formula</dt>
<dd>a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame (see model.frame).</dd>
<dt>sym.data</dt>
<dd>symbolic data table</dd>
<dt>method</dt>
<dd>cm crm</dd>
<dt>ntree</dt>
<dd>Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.</dd></dl>

Arguments

Symbolic Regression with Random Forest — sym.rf

<dl>

<dt>formula</dt>
<dd>a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame (see model.frame).</dd>


<dt>sym.data</dt>
<dd>symbolic data table</dd>


<dt>method</dt>
<dd>cm crm</dd>


<dt>ntree</dt>
<dd>Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.</dd>

</dl>

sym.rf: Symbolic Regression with Random Forest

Description

Usage

Arguments

References