CForestModel

An implementation of the random forest and bagging ensemble algorithms
utilizing conditional inference trees as base learners.

Meta-package for statistical and machine learning with a unified
interface for model fitting, prediction, performance assessment, and
presentation of results.  Approaches for model fitting and prediction of
numerical, categorical, or censored time-to-event outcomes include
traditional regression models, regularization methods, tree-based methods,
support vector machines, neural networks, ensembles, data preprocessing,
filtering, and model tuning and selection.  Performance metrics are provided
for model assessment and can be estimated with independent test sets, split
sampling, cross-validation, or bootstrap resampling.  Resample estimation
can be executed in parallel for faster processing and nested in cases of
model tuning and selection.  Modeling results can be summarized with
descriptive statistics; calibration curves; variable importance; partial
dependence plots; confusion matrices; and ROC, lift, and other performance
curves.

Brian Smith

MachineShop

Machine Learning Models and Tools

CForestModel function

<dl><dt>teststat</dt>
<dd>character specifying the type of the test statistic to be
applied.</dd>
<dt>testtype</dt>
<dd>character specifying how to compute the distribution of the
test statistic.</dd>
<dt>mincriterion</dt>
<dd>value of the test statistic that must be exceeded in
order to implement a split.</dd>
<dt>ntree</dt>
<dd>number of trees to grow in a forest.</dd>
<dt>mtry</dt>
<dd>number of input variables randomly sampled as candidates at each
node for random forest like algorithms.</dd>
<dt>replace</dt>
<dd>logical indicating whether sampling of observations is done
with or without replacement.</dd>
<dt>fraction</dt>
<dd>fraction of number of observations to draw without
replacement (only relevant if <code>replace = FALSE</code>).</dd></dl>

Arguments

Conditional Random Forest Model — CForestModel

<dl>

<dt>teststat</dt>
<dd>character specifying the type of the test statistic to be
applied.</dd>


<dt>testtype</dt>
<dd>character specifying how to compute the distribution of the
test statistic.</dd>


<dt>mincriterion</dt>
<dd>value of the test statistic that must be exceeded in
order to implement a split.</dd>


<dt>ntree</dt>
<dd>number of trees to grow in a forest.</dd>


<dt>mtry</dt>
<dd>number of input variables randomly sampled as candidates at each
node for random forest like algorithms.</dd>


<dt>replace</dt>
<dd>logical indicating whether sampling of observations is done
with or without replacement.</dd>


<dt>fraction</dt>
<dd>fraction of number of observations to draw without
replacement (only relevant if <code>replace = FALSE</code>).</dd>

</dl>

CForestModel: Conditional Random Forest Model

Description

Usage

Value

Arguments

Details

See Also

Examples