<p>A data set containing measurements of fetal heart rate and uterine
contraction from cardiotocograms.  This data set was obtained from
the [UCI machine learning repository](https://archive.ics.uci.edu/ml/index.html)
For our examples we extract a random sub sample of 100 observations.</p>

datasets

Fuzzy forests, a new algorithm based on random forests,
is designed to reduce the bias seen in random forest feature selection
caused by the presence of correlated features.  Fuzzy forests uses
recursive feature elimination random forests to select
features from separate blocks of correlated features where the
correlation within each block of features is high
and the correlation between blocks of features is low.
One final random forest is fit using the surviving features.
This package fits random forests using the 'randomForest' package and
allows for easy use of 'WGCNA' to split features into distinct blocks.
See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
for further details.

Daniel Conn

fuzzyforest

Fuzzy Forests

Tuck Ngun

Christina M. Ramirez

ctg function

<p>A data frame with 100 rows and 21.</p>

ctg: Cardiotocography Data Set

Description

Usage

Arguments

Format