## "kknn"
s=mparheuristic("kknn",n=1)
print(s)
s=mparheuristic("kknn",n=10)
print(s)
s=mparheuristic("kknn",lower=5,upper=15,by=2)
print(s)
## "mlpe"
s=mparheuristic("mlpe")
print(s) # "NA" means set size with inputs/2 in fit
s=mparheuristic("mlpe",n=10)
print(s)
## "randomForest"
s=mparheuristic("randomForest",n=10)
print(s)
## "ksvm"
s=mparheuristic("ksvm",n=10)
print(s)
s=mparheuristic("ksvm",n=10,kernel="vanilladot")
print(s)
s=mparheuristic("ksvm",n=10,kernel="polydot")
print(s)
## "rpart" and "ctree" are special cases (see help(fit,package=rminer) examples):
s=mparheuristic("rpart",n=3)
print(s)
s=mparheuristic("ctree",n=3)
print(s)
### examples with fit
## Not run:
# ### classification
# data(iris)
# s=mparheuristic("ksvm",n=3,kernel="vanilladot")
# print(s)
# search=list(search=s,method=c("holdout",2/3,123))
# M=fit(Species~.,data=iris,model="ksvm",search=search,fdebug=TRUE)
# print(M@mpar)
#
# ### regression
# data(sa_ssin)
# s=mparheuristic("ksvm",n=3,kernel="polydot")
# print(s)
# search=list(search=s,metric="MAE",method=c("holdout",2/3,123))
# M=fit(y~.,data=sa_ssin,model="ksvm",search=search,fdebug=TRUE)
# print(M@mpar)
# ## End(Not run)
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