projectLearnerToGrid: create learned tesselation of feature space after PC...
In MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

Description Usage Arguments Value Author(s) References Examples

create learned tesselation of feature space after PC transformation

projectLearnerToGrid(formula, data, learnerSchema, 
   trainInds, ..., 
   dropIntercept = TRUE, 
   ngpts = 20, 
   predExtras = list(), 
   predWrapper = force)

`formula`	standard formula, typically of the form "y~." where y denotes the class label variable to be predicted by all remaining features in the input data frame
`data`	a data.frame instance
`learnerSchema`	an instance of `learnerSchema-class`
`trainInds`	integer vector of rows of `data` to use for training
`...`	additional parameters for use with `learnerSchema`
`dropIntercept`	logical indicating whether to include column of 1s among feature column-vectors
`ngpts`	number of equispaced points along the range of each input feature to use in forming a grid in feature space
`predExtras`	a list with named elements giving binding to extra parameters needed to predict labels for the learner in use. For example, with `ldaI`, set `predExtras=list(type="class")`
`predWrapper`	Sometimes a function call is needed to extract the predicted labels from the RObject applied to the `fittedLearner` slot of the output object; this parameter defines that call.

instance of projectedLearner-class

VJ Carey <stvjc@channing.harvard.edu>

none.

library(mlbench)
# demostrate with 3 dimensional hypercube problem
kk = mlbench.hypercube()
colnames(kk$x) = c("f1", "f2", "f3")
hcu = data.frame(cl=kk$classes, kk$x)
set.seed(1234)
sam = sample(1:nrow(kk$x), size=nrow(kk$x)/2)
ldap = projectLearnerToGrid(cl~., data=hcu, ldaI, 
   sam, predWrapper=function(x)x$class)
plot(ldap)
confuMat(ldap@fittedLearner)
nnetp = projectLearnerToGrid(cl~., data=hcu, nnetI, sam, size=2,
   decay=.01, predExtras=list(type="class"))
plot(nnetp)
confuMat(nnetp@fittedLearner)
#if (requireNamespace("rgl") && interactive()) {
#    learnerIn3D(nnetp)
#    ## customising the rgl plot
#    learnerIn3D(nnetp, size = 10, alpha = 0.1)
#}