% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{experiment_cor_vs_vif}
\alias{experiment_cor_vs_vif}
\title{Dataframe with results of experiment comparing correlation and VIF thresholds}
\format{
A dataframe with 10,000 rows and 6 variables:
\describe{
\item{input_rows}{Number of rows in the input data subset.}
\item{input_predictors}{Number of predictors in the input data subset.}
\item{output_predictors}{Number of predictors selected by \code{\link[=vif_select]{vif_select()}} at the best-matching \code{max_vif}.}
\item{max_cor}{Maximum allowed pairwise correlation supplied to \code{\link[=cor_select]{cor_select()}}.}
\item{max_vif}{VIF threshold at which \code{\link[=vif_select]{vif_select()}} produced the highest Jaccard similarity with \code{\link[=cor_select]{cor_select()}} for the given \code{max_cor}.}
\item{out_selection_jaccard}{Jaccard similarity between the predictors selected by \code{\link[=cor_select]{cor_select()}} and \code{\link[=vif_select]{vif_select()}}.}
}
}
\usage{
data(experiment_cor_vs_vif)
}
\description{
A dataframe summarizing 10,000 experiments comparing the output of \code{\link[=cor_select]{cor_select()}} and \code{\link[=vif_select]{vif_select()}}. Each row records the input sampling parameters and the resulting feature-selection metrics.
}
\details{
The source data is a synthetic dataframe with 500 columns and 10,000 rows generated using \code{distantia::zoo_simulate()} with correlated time series (\code{independent = FALSE}).

Each iteration randomly subsets 10-50 predictors and 30-100 rows per predictor, applies \code{\link[=cor_select]{cor_select()}} with a random \code{max_cor} threshold, then finds the \code{max_vif} value that maximizes Jaccard similarity between the two selections.
}
\examples{
data(experiment_cor_vs_vif)
str(experiment_cor_vs_vif)
}
\seealso{
Other experiments:
\code{\link{experiment_adaptive_thresholds}},
\code{\link{gam_cor_to_vif}},
\code{\link{prediction_cor_to_vif}}
}
\concept{experiments}
\keyword{datasets}
