ggplot.rfe {caret} | R Documentation |
Plot RFE Performance Profiles
Description
These functions plot the resampling results for the candidate subset sizes evaluated during the recursive feature elimination (RFE) process
Usage
## S3 method for class 'rfe'
ggplot(
data = NULL,
mapping = NULL,
metric = data$metric[1],
output = "layered",
...,
environment = NULL
)
## S3 method for class 'rfe'
plot(x, metric = x$metric, ...)
Arguments
data |
an object of class |
mapping , environment |
unused arguments to make consistent with ggplot2 generic method |
metric |
What measure of performance to plot. Examples of possible values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be used depending on what metrics have been calculated. |
output |
either "data", "ggplot" or "layered". The first returns a data
frame while the second returns a simple |
... |
|
x |
an object of class |
Details
These plots show the average performance versus the subset sizes.
Value
a lattice or ggplot object
Note
We using a recipe as an input, there may be some subset sizes that are not well-replicated over resamples. The 'ggplot' method will only show subset sizes where at least half of the resamples have associated results.
Author(s)
Max Kuhn
References
Kuhn (2008), “Building Predictive Models in R Using the caret” (doi:10.18637/jss.v028.i05)
See Also
Examples
## Not run:
data(BloodBrain)
x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)])
x <- x[, -findCorrelation(cor(x), .8)]
x <- as.data.frame(x, stringsAsFactors = TRUE)
set.seed(1)
lmProfile <- rfe(x, logBBB,
sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65),
rfeControl = rfeControl(functions = lmFuncs,
number = 200))
plot(lmProfile)
plot(lmProfile, metric = "Rsquared")
ggplot(lmProfile)
## End(Not run)