createRandomForestFeatureSelection

Create the settings for random foreat based feature selection

A user friendly way to create patient level prediction models using
the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
of interest and an outcome of interest, the package can use data in the Common
Data Model to build a large set of features. These features can then be used to
fit a predictive model with a number of machine learning algorithms. This is
further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Egill Fridgeirsson

PatientLevelPrediction

Develop Clinical Prediction Models Using the Common Data Model

Jenna Reps

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

Observational Health Data Science and Informatics 

createRandomForestFeatureSelection function

<dl><dt>ntrees</dt>
<dd>number of tree in forest</dd>
<dt>maxDepth</dt>
<dd>MAx depth of each tree</dd></dl>

Arguments

Create the settings for random foreat based feature selection — createRandomForestFeatureSelection

<dl>

<dt>ntrees</dt>
<dd>number of tree in forest</dd>


<dt>maxDepth</dt>
<dd>MAx depth of each tree</dd>

</dl>

createRandomForestFeatureSelection: Create the settings for random foreat based feature selection

Description

Usage

Value

Arguments

Details