computeAuprc

Compute the area under the Precision-Recall curve

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 

computeAuprc function

<dl><dt>prediction</dt>
<dd>A prediction object as generated using the
<code><a href="/link/predict?package=PatientLevelPrediction&version=6.6.0" data-mini-rdoc="PatientLevelPrediction::predict">predict</a></code> functions.</dd></dl>

Arguments

Compute the area under the Precision-Recall curve — computeAuprc

<dl>

<dt>prediction</dt>
<dd>A prediction object as generated using the
<code><a href='https://rdrr.io/r/stats/predict.html'>predict</a></code> functions.</dd>

</dl>

computeAuprc: Compute the area under the Precision-Recall curve

Description

Usage

Value

Arguments

Details

Examples