Description Usage Arguments Details Value Examples
View source: R/06ResultClass.R
preprocomb executes the computation of classification accuracy, hopkins statistic and ORH outlier score. An alternative to preprocomb is to use package 'metaheur' for faster finding of near-optimal combinations.
1 2 3 | preprocomb(models = "rpart", gridclassobject, nholdout = 2,
searchmethod = "exhaustive", predict = TRUE, cluster = FALSE,
cores = 1)
|
models |
(character) vector of models (names of models as defined in package caret), defaults to "rpart" |
gridclassobject |
(GridClass) object representing the grid of combinations |
nholdout |
(integer) number of holdout rounds for predictive classification, must be two or more, defaults to two |
searchmethod |
(character) defaults to "exhaustive" full blind search, "random" search 20 percent of grid, "grid" grid search 10 percent |
predict |
(boolean) compute predictions, defaults to TRUE |
cluster |
(boolean) compute clustering tendency, defaults to FALSE |
cores |
(integer) number of cores used in parallel processing of holdout rounds, defaults to 1 |
caret messages will be displayed during processing
a ResultClass object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## modifiediris <- droplevels(iris[-c(1:60),])
## grid <- setgrid(phases=c("outliers", "scaling"), data=modifiediris)
## library(kernlab)
## result <- preprocomb(models=c("svmRadial"), grid=grid, nholdout=1, search="grid")
## result@allclassification
## result@allclustering
## result@alloutliers
## result@rawall
## result@catclassification
##
## newphases <- c("outliers", "smoothing", "scaling", "selection", "sampling")
## newmodels <- c("knn", "rf", "svmRadial")
## grid1 <- setgrid(phases=newphases, data=modifiediris)
## result1 <- preprocomb(models=newmodels, grid=grid1, nholdout=1, search="grid")
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