minMaxNormalize

A function that normalizes continous features to have values between 0 and 1

internal

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 

minMaxNormalize function

<dl><dt>trainData</dt>
<dd>The training data to be normalized</dd>
<dt>featureEngineeringSettings</dt>
<dd>The settings for the normalization</dd>
<dt>done</dt>
<dd>Whether the data has already been normalized (bool)</dd></dl>

Arguments

A function that normalizes continous features to have values between 0 and 1 — minMaxNormalize

<dl>

<dt>trainData</dt>
<dd>The training data to be normalized</dd>


<dt>featureEngineeringSettings</dt>
<dd>The settings for the normalization</dd>


<dt>done</dt>
<dd>Whether the data has already been normalized (bool)</dd>

</dl>

minMaxNormalize: A function that normalizes continous features to have values between 0 and 1

Description

Usage

Value

Arguments

Details