javed khan
2020-Mar-03 20:27 UTC
[R] Getting error message, "LOOCV is not compatible with `resamples()` since only one resampling estimate is available. "
The data is as follows: I included the code for 10 fold CV and LOOCV
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15), Language = c(1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,
1, 1, 3), Hardware = c(1, 2, 3, 1, 2, 4, 4, 2, 1, 1, 1, 5, 6,
1, 1), Duration = c(17, 7, 15, 18, 13, 5, 5, 11, 14, 5, 13, 31,
20, 26, 14), KSLOC = c(253.6, 40.5, 450, 214.4, 449.9, 50, 43,
200, 289, 39, 254.2, 128.6, 161.4, 164.8, 60.2), AdjFP = c(1217.1,
507.3, 2306.8, 788.5, 1337.6, 421.3, 99.9, 993, 1592.9, 240,
1611, 789, 690.9, 1347.5, 1044.3), RAWFP = c(1010, 457, 2284,
881, 1583, 411, 97, 998, 1554, 250, 1603, 724, 705, 1375, 976
), EffortMM = c(287, 82.5, 1107.31, 86.9, 336.3, 84, 23.2, 130.3,
116, 72, 258.7, 230.7, 157, 246.9, 69.9)), class = "data.frame",
row.names
= c(NA,
-15L))
index <- createDataPartition(d$Effort, p = .70,list = FALSE)
tr <- d[index, ]
ts <- d[-index, ]
index_2 <- createFolds(tr$Effort, returnTrain = TRUE, list = TRUE)
ct_rand <- trainControl(method = "repeatedcv", number=10,
repeats=10,index
= index_2, search="random")
ct_grid <- trainControl(method = "repeatedcv", number=10,
repeats=10,index
= index_2, search="grid")
ct_locv <- trainControl(method = "LOOCV",
search="random")
ct_locv2 <- trainControl(method = "LOOCV",
search="grid")
## ## ## ## ##Random Search for for 10 fold CV
set.seed(30218)
ran_CV <- train(Effort ~ ., data = tr,
method = "pls",
tuneLength = 15,
metric = "MAE",
preProc = c("center", "scale",
"zv"),
trControl = ct_rand)
getTrainPerf(ran_CV)
rn <- predict(ran_CV, newdata = ts)
##MAE(rn, ts$Effort)
## ## ## ## ##grid search for 10 fold CV
set.seed(30218)
grid_CV <- train(Effort ~ ., data = tr,
method = "pls",
metric = "MAE",
preProc = c("center", "scale",
"zv"),
trControl = ct_grid)
getTrainPerf(grid_CV)
## ## ## ## ##Random Search for LOOCV
set.seed(30218)
ran_locv <- train(Effort ~ ., data = tr,
method = "pls",
tuneLength = 15,
metric = "MAE",
preProc = c("center", "scale",
"zv"),
trControl = ct_locv)
getTrainPerf(ran_locv)
rn <- predict(ran_search, newdata = ts)
##MAE(rn, ts$Effort)
## ## ## ## ##Grid Search for LOOCV
set.seed(30218)
grid_locv <- train(Effort ~ ., data = tr,
method = "pls",
metric = "MAE",
preProc = c("center", "scale",
"zv"),
trControl = ct_locv2)
getTrainPerf(grid_locv)
rValues <- resamples(list(Random_Search_CV=ran_CV, Grid_Search_CV=grid_CV,
Random_Search_LOOCV=ran_locv,
Grid_Search_LOOCV=grid_locv))
bwplot(rValues,metric="MAE", scales=list(cex=1),
col="Green")
On Tue, Mar 3, 2020 at 5:07 PM Bert Gunter <bgunter.4567 at gmail.com>
wrote:
> 2 1/2 suggestions:
>
> 1. Provide a small reproducible example with **minimal code** . It can be
> difficult to sort through dozens of lines of code, and I, anyway, would be
> unwilling to spend time trying to debug/isolate the problem when you have
> apparently not made much of an effort to do so yourself. Others may well be
> both more knowledgeable and more tolerant, of course.
>
> 2. If, **after a suitable wait ** you have not received useful answers,
> contact the package maintainer of the package you used **which you have
> again failed to identify** (the caret package?) . Also check to see whether
> the package has its own user support structure. Some do, and this should be
> the first point of contact anyway if so.
>
> 2 1/2 . Post in **plain text** not html, though I don't think it
mattered
> here.
>
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
and
> sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip
)
>
>
> On Tue, Mar 3, 2020 at 3:30 AM javed khan <javedbtk111 at gmail.com>
wrote:
>
>> Hi, I am using different validation methods for random search and grid
>> search. The validation methods are 10 fold CV, bootstrap and LOOCV but
for
>> LOOCV, I get the error message when I draw boxplots for all the
results.
>>
>> Error is , LOOCV is not compatible with `resamples()` since only one
>> resampling estimate is available.
>>
>> The code is below.
>>
>> d=readARFF("china.arff")
>> index <- createDataPartition(d$Effort, p = .70,list = FALSE)
>> tr <- d[index, ]
>> ts <- d[-index, ]
>> index_2 <- createFolds(tr$Effort, returnTrain = TRUE, list = TRUE)
>>
>>
>>
>>
>> ct_rand <- trainControl(method = "repeatedcv", number=10,
repeats=10,index
>> = index_2, search="random")
>> ct_grid <- trainControl(method = "repeatedcv", number=10,
repeats=10,index
>> = index_2, search="grid")
>>
>>
>> ct_boot1 <- trainControl(method = "boot", number=100,
index = index_2,
>> search="random")
>> ct_boot2 <- trainControl(method = "boot", number=100,
index = index_2,
>> search="grid")
>>
>> ct_locv <- trainControl(method = "LOOCV",
search="random")
>> ct_locv2 <- trainControl(method = "LOOCV",
search="grid")
>>
>> set.seed(30218)
>> ran_CV <- train(Effort ~ ., data = tr,
>> method = "pls",
>> tuneLength = 15,
>> metric = "MAE",
>> preProc = c("center", "scale",
"zv"),
>> trControl = ct_rand)
>> getTrainPerf(ran_CV)
>> rn <- predict(ran_CV, newdata = ts)
>>
>> ## ## ## ## ##grid search CV
>>
>> set.seed(30218)
>> grid_CV <- train(Effort ~ ., data = tr,
>> method = "pls",
>> metric = "MAE",
>> preProc = c("center", "scale",
"zv"),
>> trControl = ct_grid)
>>
>> getTrainPerf(grid_CV)
>>
>> set.seed(30218)
>> ran_boot <- train(Effort ~ ., data = tr,
>> method = "pls",
>> tuneLength = 15,
>> metric = "MAE",
>> preProc = c("center", "scale",
"zv"),
>> trControl = ct_boot1)
>> getTrainPerf(ran_boot)
>> rn <- predict(ran_search, newdata = ts)
>> ##MAE(rn, ts$Effort)
>>
>>
>> ## ## ## ## ##grid search boot
>>
>> set.seed(30218)
>> grid_boot <- train(Effort ~ ., data = tr,
>> method = "pls",
>> metric = "MAE",
>> preProc = c("center", "scale",
"zv"),
>> trControl = ct_boot2)
>>
>> getTrainPerf(grid_boot)
>>
>>
>> set.seed(30218)
>> ran_locv <- train(Effort ~ ., data = tr,
>> method = "pls",
>> tuneLength = 15,
>> metric = "MAE",
>> preProc = c("center", "scale",
"zv"),
>> trControl = ct_locv)
>> getTrainPerf(ran_locv)
>> rn <- predict(ran_search, newdata = ts)
>> ##MAE(rn, ts$Effort)
>>
>>
>> ## ## ## ## ##grid search CV
>>
>> set.seed(30218)
>> grid_locv <- train(Effort ~ ., data = tr,
>> method = "pls",
>> metric = "MAE",
>> preProc = c("center", "scale",
"zv"),
>> trControl = ct_locv2)
>>
>> getTrainPerf(grid_locv)
>>
>>
>> rValues <- resamples(list(Random_Search_CV=ran_CV,
Grid_Search_CV=grid_CV,
>> Random_Search_Boot=ran_boot, Grid_Search_Boot=grid_boot ,
>> Random_Search_LOOCV=ran_locv,
>> Grid_Search_LOOCV=grid_locv))
>>
>> bwplot(rValues,metric="MAE", scales=list(cex=1),
col="Green")
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
[[alternative HTML version deleted]]
Bert Gunter
2020-Mar-03 20:57 UTC
[R] Getting error message, "LOOCV is not compatible with `resamples()` since only one resampling estimate is available. "
... and you **still** have not told us what package(s)... Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Mar 3, 2020 at 12:28 PM javed khan <javedbtk111 at gmail.com> wrote:> The data is as follows: I included the code for 10 fold CV and LOOCV > > structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, > 13, 14, 15), Language = c(1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, > 1, 1, 3), Hardware = c(1, 2, 3, 1, 2, 4, 4, 2, 1, 1, 1, 5, 6, > 1, 1), Duration = c(17, 7, 15, 18, 13, 5, 5, 11, 14, 5, 13, 31, > 20, 26, 14), KSLOC = c(253.6, 40.5, 450, 214.4, 449.9, 50, 43, > 200, 289, 39, 254.2, 128.6, 161.4, 164.8, 60.2), AdjFP = c(1217.1, > 507.3, 2306.8, 788.5, 1337.6, 421.3, 99.9, 993, 1592.9, 240, > 1611, 789, 690.9, 1347.5, 1044.3), RAWFP = c(1010, 457, 2284, > 881, 1583, 411, 97, 998, 1554, 250, 1603, 724, 705, 1375, 976 > ), EffortMM = c(287, 82.5, 1107.31, 86.9, 336.3, 84, 23.2, 130.3, > 116, 72, 258.7, 230.7, 157, 246.9, 69.9)), class = "data.frame", row.names > = c(NA, > -15L)) > > > index <- createDataPartition(d$Effort, p = .70,list = FALSE) > tr <- d[index, ] > ts <- d[-index, ] > index_2 <- createFolds(tr$Effort, returnTrain = TRUE, list = TRUE) > > ct_rand <- trainControl(method = "repeatedcv", number=10, repeats=10,index > = index_2, search="random") > ct_grid <- trainControl(method = "repeatedcv", number=10, repeats=10,index > = index_2, search="grid") > > ct_locv <- trainControl(method = "LOOCV", search="random") > ct_locv2 <- trainControl(method = "LOOCV", search="grid") > > ## ## ## ## ##Random Search for for 10 fold CV > > set.seed(30218) > ran_CV <- train(Effort ~ ., data = tr, > method = "pls", > tuneLength = 15, > metric = "MAE", > preProc = c("center", "scale", "zv"), > trControl = ct_rand) > getTrainPerf(ran_CV) > rn <- predict(ran_CV, newdata = ts) > ##MAE(rn, ts$Effort) > > > ## ## ## ## ##grid search for 10 fold CV > > set.seed(30218) > grid_CV <- train(Effort ~ ., data = tr, > method = "pls", > metric = "MAE", > preProc = c("center", "scale", "zv"), > trControl = ct_grid) > > getTrainPerf(grid_CV) > > ## ## ## ## ##Random Search for LOOCV > > set.seed(30218) > ran_locv <- train(Effort ~ ., data = tr, > method = "pls", > tuneLength = 15, > metric = "MAE", > preProc = c("center", "scale", "zv"), > trControl = ct_locv) > getTrainPerf(ran_locv) > rn <- predict(ran_search, newdata = ts) > ##MAE(rn, ts$Effort) > > > ## ## ## ## ##Grid Search for LOOCV > > set.seed(30218) > grid_locv <- train(Effort ~ ., data = tr, > method = "pls", > metric = "MAE", > preProc = c("center", "scale", "zv"), > trControl = ct_locv2) > > getTrainPerf(grid_locv) > > rValues <- resamples(list(Random_Search_CV=ran_CV, Grid_Search_CV=grid_CV, > > Random_Search_LOOCV=ran_locv, > Grid_Search_LOOCV=grid_locv)) > > bwplot(rValues,metric="MAE", scales=list(cex=1), col="Green") > > > > > On Tue, Mar 3, 2020 at 5:07 PM Bert Gunter <bgunter.4567 at gmail.com> wrote: > >> 2 1/2 suggestions: >> >> 1. Provide a small reproducible example with **minimal code** . It can be >> difficult to sort through dozens of lines of code, and I, anyway, would be >> unwilling to spend time trying to debug/isolate the problem when you have >> apparently not made much of an effort to do so yourself. Others may well be >> both more knowledgeable and more tolerant, of course. >> >> 2. If, **after a suitable wait ** you have not received useful answers, >> contact the package maintainer of the package you used **which you have >> again failed to identify** (the caret package?) . Also check to see whether >> the package has its own user support structure. Some do, and this should be >> the first point of contact anyway if so. >> >> 2 1/2 . Post in **plain text** not html, though I don't think it mattered >> here. >> >> >> Bert Gunter >> >> "The trouble with having an open mind is that people keep coming along >> and sticking things into it." >> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) >> >> >> On Tue, Mar 3, 2020 at 3:30 AM javed khan <javedbtk111 at gmail.com> wrote: >> >>> Hi, I am using different validation methods for random search and grid >>> search. The validation methods are 10 fold CV, bootstrap and LOOCV but >>> for >>> LOOCV, I get the error message when I draw boxplots for all the results. >>> >>> Error is , LOOCV is not compatible with `resamples()` since only one >>> resampling estimate is available. >>> >>> The code is below. >>> >>> d=readARFF("china.arff") >>> index <- createDataPartition(d$Effort, p = .70,list = FALSE) >>> tr <- d[index, ] >>> ts <- d[-index, ] >>> index_2 <- createFolds(tr$Effort, returnTrain = TRUE, list = TRUE) >>> >>> >>> >>> >>> ct_rand <- trainControl(method = "repeatedcv", number=10, >>> repeats=10,index >>> = index_2, search="random") >>> ct_grid <- trainControl(method = "repeatedcv", number=10, >>> repeats=10,index >>> = index_2, search="grid") >>> >>> >>> ct_boot1 <- trainControl(method = "boot", number=100, index = index_2, >>> search="random") >>> ct_boot2 <- trainControl(method = "boot", number=100, index = index_2, >>> search="grid") >>> >>> ct_locv <- trainControl(method = "LOOCV", search="random") >>> ct_locv2 <- trainControl(method = "LOOCV", search="grid") >>> >>> set.seed(30218) >>> ran_CV <- train(Effort ~ ., data = tr, >>> method = "pls", >>> tuneLength = 15, >>> metric = "MAE", >>> preProc = c("center", "scale", "zv"), >>> trControl = ct_rand) >>> getTrainPerf(ran_CV) >>> rn <- predict(ran_CV, newdata = ts) >>> >>> ## ## ## ## ##grid search CV >>> >>> set.seed(30218) >>> grid_CV <- train(Effort ~ ., data = tr, >>> method = "pls", >>> metric = "MAE", >>> preProc = c("center", "scale", "zv"), >>> trControl = ct_grid) >>> >>> getTrainPerf(grid_CV) >>> >>> set.seed(30218) >>> ran_boot <- train(Effort ~ ., data = tr, >>> method = "pls", >>> tuneLength = 15, >>> metric = "MAE", >>> preProc = c("center", "scale", "zv"), >>> trControl = ct_boot1) >>> getTrainPerf(ran_boot) >>> rn <- predict(ran_search, newdata = ts) >>> ##MAE(rn, ts$Effort) >>> >>> >>> ## ## ## ## ##grid search boot >>> >>> set.seed(30218) >>> grid_boot <- train(Effort ~ ., data = tr, >>> method = "pls", >>> metric = "MAE", >>> preProc = c("center", "scale", "zv"), >>> trControl = ct_boot2) >>> >>> getTrainPerf(grid_boot) >>> >>> >>> set.seed(30218) >>> ran_locv <- train(Effort ~ ., data = tr, >>> method = "pls", >>> tuneLength = 15, >>> metric = "MAE", >>> preProc = c("center", "scale", "zv"), >>> trControl = ct_locv) >>> getTrainPerf(ran_locv) >>> rn <- predict(ran_search, newdata = ts) >>> ##MAE(rn, ts$Effort) >>> >>> >>> ## ## ## ## ##grid search CV >>> >>> set.seed(30218) >>> grid_locv <- train(Effort ~ ., data = tr, >>> method = "pls", >>> metric = "MAE", >>> preProc = c("center", "scale", "zv"), >>> trControl = ct_locv2) >>> >>> getTrainPerf(grid_locv) >>> >>> >>> rValues <- resamples(list(Random_Search_CV=ran_CV, >>> Grid_Search_CV=grid_CV, >>> Random_Search_Boot=ran_boot, Grid_Search_Boot=grid_boot , >>> Random_Search_LOOCV=ran_locv, >>> Grid_Search_LOOCV=grid_locv)) >>> >>> bwplot(rValues,metric="MAE", scales=list(cex=1), col="Green") >>> >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >>[[alternative HTML version deleted]]
javed khan
2020-Mar-03 21:57 UTC
[R] Getting error message, "LOOCV is not compatible with `resamples()` since only one resampling estimate is available. "
I am sorry for that... I am just using caret package. Thanks On Tue, Mar 3, 2020 at 9:57 PM Bert Gunter <bgunter.4567 at gmail.com> wrote:> ... and you **still** have not told us what package(s)... > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Tue, Mar 3, 2020 at 12:28 PM javed khan <javedbtk111 at gmail.com> wrote: > >> The data is as follows: I included the code for 10 fold CV and LOOCV >> >> structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, >> 13, 14, 15), Language = c(1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, >> 1, 1, 3), Hardware = c(1, 2, 3, 1, 2, 4, 4, 2, 1, 1, 1, 5, 6, >> 1, 1), Duration = c(17, 7, 15, 18, 13, 5, 5, 11, 14, 5, 13, 31, >> 20, 26, 14), KSLOC = c(253.6, 40.5, 450, 214.4, 449.9, 50, 43, >> 200, 289, 39, 254.2, 128.6, 161.4, 164.8, 60.2), AdjFP = c(1217.1, >> 507.3, 2306.8, 788.5, 1337.6, 421.3, 99.9, 993, 1592.9, 240, >> 1611, 789, 690.9, 1347.5, 1044.3), RAWFP = c(1010, 457, 2284, >> 881, 1583, 411, 97, 998, 1554, 250, 1603, 724, 705, 1375, 976 >> ), EffortMM = c(287, 82.5, 1107.31, 86.9, 336.3, 84, 23.2, 130.3, >> 116, 72, 258.7, 230.7, 157, 246.9, 69.9)), class = "data.frame", >> row.names = c(NA, >> -15L)) >> >> >> index <- createDataPartition(d$Effort, p = .70,list = FALSE) >> tr <- d[index, ] >> ts <- d[-index, ] >> index_2 <- createFolds(tr$Effort, returnTrain = TRUE, list = TRUE) >> >> ct_rand <- trainControl(method = "repeatedcv", number=10, >> repeats=10,index = index_2, search="random") >> ct_grid <- trainControl(method = "repeatedcv", number=10, >> repeats=10,index = index_2, search="grid") >> >> ct_locv <- trainControl(method = "LOOCV", search="random") >> ct_locv2 <- trainControl(method = "LOOCV", search="grid") >> >> ## ## ## ## ##Random Search for for 10 fold CV >> >> set.seed(30218) >> ran_CV <- train(Effort ~ ., data = tr, >> method = "pls", >> tuneLength = 15, >> metric = "MAE", >> preProc = c("center", "scale", "zv"), >> trControl = ct_rand) >> getTrainPerf(ran_CV) >> rn <- predict(ran_CV, newdata = ts) >> ##MAE(rn, ts$Effort) >> >> >> ## ## ## ## ##grid search for 10 fold CV >> >> set.seed(30218) >> grid_CV <- train(Effort ~ ., data = tr, >> method = "pls", >> metric = "MAE", >> preProc = c("center", "scale", "zv"), >> trControl = ct_grid) >> >> getTrainPerf(grid_CV) >> >> ## ## ## ## ##Random Search for LOOCV >> >> set.seed(30218) >> ran_locv <- train(Effort ~ ., data = tr, >> method = "pls", >> tuneLength = 15, >> metric = "MAE", >> preProc = c("center", "scale", "zv"), >> trControl = ct_locv) >> getTrainPerf(ran_locv) >> rn <- predict(ran_search, newdata = ts) >> ##MAE(rn, ts$Effort) >> >> >> ## ## ## ## ##Grid Search for LOOCV >> >> set.seed(30218) >> grid_locv <- train(Effort ~ ., data = tr, >> method = "pls", >> metric = "MAE", >> preProc = c("center", "scale", "zv"), >> trControl = ct_locv2) >> >> getTrainPerf(grid_locv) >> >> rValues <- resamples(list(Random_Search_CV=ran_CV, >> Grid_Search_CV=grid_CV, >> >> Random_Search_LOOCV=ran_locv, >> Grid_Search_LOOCV=grid_locv)) >> >> bwplot(rValues,metric="MAE", scales=list(cex=1), col="Green") >> >> >> >> >> On Tue, Mar 3, 2020 at 5:07 PM Bert Gunter <bgunter.4567 at gmail.com> >> wrote: >> >>> 2 1/2 suggestions: >>> >>> 1. Provide a small reproducible example with **minimal code** . It can >>> be difficult to sort through dozens of lines of code, and I, anyway, would >>> be unwilling to spend time trying to debug/isolate the problem when you >>> have apparently not made much of an effort to do so yourself. Others may >>> well be both more knowledgeable and more tolerant, of course. >>> >>> 2. If, **after a suitable wait ** you have not received useful answers, >>> contact the package maintainer of the package you used **which you have >>> again failed to identify** (the caret package?) . Also check to see whether >>> the package has its own user support structure. Some do, and this should be >>> the first point of contact anyway if so. >>> >>> 2 1/2 . Post in **plain text** not html, though I don't think it >>> mattered here. >>> >>> >>> Bert Gunter >>> >>> "The trouble with having an open mind is that people keep coming along >>> and sticking things into it." >>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) >>> >>> >>> On Tue, Mar 3, 2020 at 3:30 AM javed khan <javedbtk111 at gmail.com> wrote: >>> >>>> Hi, I am using different validation methods for random search and grid >>>> search. The validation methods are 10 fold CV, bootstrap and LOOCV but >>>> for >>>> LOOCV, I get the error message when I draw boxplots for all the results. >>>> >>>> Error is , LOOCV is not compatible with `resamples()` since only one >>>> resampling estimate is available. >>>> >>>> The code is below. >>>> >>>> d=readARFF("china.arff") >>>> index <- createDataPartition(d$Effort, p = .70,list = FALSE) >>>> tr <- d[index, ] >>>> ts <- d[-index, ] >>>> index_2 <- createFolds(tr$Effort, returnTrain = TRUE, list = TRUE) >>>> >>>> >>>> >>>> >>>> ct_rand <- trainControl(method = "repeatedcv", number=10, >>>> repeats=10,index >>>> = index_2, search="random") >>>> ct_grid <- trainControl(method = "repeatedcv", number=10, >>>> repeats=10,index >>>> = index_2, search="grid") >>>> >>>> >>>> ct_boot1 <- trainControl(method = "boot", number=100, index = index_2, >>>> search="random") >>>> ct_boot2 <- trainControl(method = "boot", number=100, index = index_2, >>>> search="grid") >>>> >>>> ct_locv <- trainControl(method = "LOOCV", search="random") >>>> ct_locv2 <- trainControl(method = "LOOCV", search="grid") >>>> >>>> set.seed(30218) >>>> ran_CV <- train(Effort ~ ., data = tr, >>>> method = "pls", >>>> tuneLength = 15, >>>> metric = "MAE", >>>> preProc = c("center", "scale", "zv"), >>>> trControl = ct_rand) >>>> getTrainPerf(ran_CV) >>>> rn <- predict(ran_CV, newdata = ts) >>>> >>>> ## ## ## ## ##grid search CV >>>> >>>> set.seed(30218) >>>> grid_CV <- train(Effort ~ ., data = tr, >>>> method = "pls", >>>> metric = "MAE", >>>> preProc = c("center", "scale", "zv"), >>>> trControl = ct_grid) >>>> >>>> getTrainPerf(grid_CV) >>>> >>>> set.seed(30218) >>>> ran_boot <- train(Effort ~ ., data = tr, >>>> method = "pls", >>>> tuneLength = 15, >>>> metric = "MAE", >>>> preProc = c("center", "scale", "zv"), >>>> trControl = ct_boot1) >>>> getTrainPerf(ran_boot) >>>> rn <- predict(ran_search, newdata = ts) >>>> ##MAE(rn, ts$Effort) >>>> >>>> >>>> ## ## ## ## ##grid search boot >>>> >>>> set.seed(30218) >>>> grid_boot <- train(Effort ~ ., data = tr, >>>> method = "pls", >>>> metric = "MAE", >>>> preProc = c("center", "scale", "zv"), >>>> trControl = ct_boot2) >>>> >>>> getTrainPerf(grid_boot) >>>> >>>> >>>> set.seed(30218) >>>> ran_locv <- train(Effort ~ ., data = tr, >>>> method = "pls", >>>> tuneLength = 15, >>>> metric = "MAE", >>>> preProc = c("center", "scale", "zv"), >>>> trControl = ct_locv) >>>> getTrainPerf(ran_locv) >>>> rn <- predict(ran_search, newdata = ts) >>>> ##MAE(rn, ts$Effort) >>>> >>>> >>>> ## ## ## ## ##grid search CV >>>> >>>> set.seed(30218) >>>> grid_locv <- train(Effort ~ ., data = tr, >>>> method = "pls", >>>> metric = "MAE", >>>> preProc = c("center", "scale", "zv"), >>>> trControl = ct_locv2) >>>> >>>> getTrainPerf(grid_locv) >>>> >>>> >>>> rValues <- resamples(list(Random_Search_CV=ran_CV, >>>> Grid_Search_CV=grid_CV, >>>> Random_Search_Boot=ran_boot, Grid_Search_Boot=grid_boot , >>>> Random_Search_LOOCV=ran_locv, >>>> Grid_Search_LOOCV=grid_locv)) >>>> >>>> bwplot(rValues,metric="MAE", scales=list(cex=1), col="Green") >>>> >>>> [[alternative HTML version deleted]] >>>> >>>> ______________________________________________ >>>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >>>> https://stat.ethz.ch/mailman/listinfo/r-help >>>> PLEASE do read the posting guide >>>> http://www.R-project.org/posting-guide.html >>>> and provide commented, minimal, self-contained, reproducible code. >>>> >>>[[alternative HTML version deleted]]