R help - Jun 2020 - kernlab ksvm rbfdot kernel - prediction returning fewer rows than provided for input

Dear R-experts,

I am trying to find the best span for my loess regression. Here below a
reproducible example. I don't get the result. Am I missing something ?

Many thanks for your help.

############################################################
a<-c(2,3,4,3,2,6,5,7,4,5,12,13,21,6,4,5,6,7)
b<-c(12,13,32,14,23,21,76,34,12,32,43,23,12,32,43,12,32,11)
model <- loess(b ~ a)
bestLoess <- function(model, spans = c(.05, .95)) {
?f <- function(span) {
???????? mod <- update(model, span = span)
???????? loessGCV(mod)[["gcv"]]
???? }
???? result <- optimize(f, spans)
???? result
?}
#######################################################################

On Tue, 14 Apr 2020 21:00:34 +0000 (UTC)
varin sacha via R-help <r-help at r-project.org> wrote:
> Here below a reproducible example. I don't get the result.
Thanks for providing a concise piece of code.

The code doesn't return any visible results because the bestLoess
function created by it is never called. To get the results you should
call the function and pass the model object to it as an argument.

Another problem with the code is missing definition for loessGCV
function. I can only assume that the code is supposed to reference the
recently archived NormalizeMets CRAN package.

-- 
Best regards,
Ivan

Dear Ivan,

Many thanks I got it now. 

Best,




Le mercredi 15 avril 2020 ? 09:49:26 UTC+2, Ivan Krylov <krylov.r00t at
gmail.com> a ?crit :





On Tue, 14 Apr 2020 21:00:34 +0000 (UTC)

varin sacha via R-help <r-help at r-project.org> wrote:
> Here below a reproducible example. I don't get the result.

Thanks for providing a concise piece of code.

The code doesn't return any visible results because the bestLoess
function created by it is never called. To get the results you should
call the function and pass the model object to it as an argument.

Another problem with the code is missing definition for loessGCV
function. I can only assume that the code is supposed to reference the
recently archived NormalizeMets CRAN package.

-- 
Best regards,
Ivan

Hi everyone. I'm using the kernlab ksvm function with the rbfdot  
kernel for a binary classification problem and getting a strange  
result back. The predictions seem to be very accurate judging by the  
training results provided by the algorithm, but I'm unable to generate  
a confusion matrix because there is a difference in the number of  
output records from my model test compared to what was input into the  
test dataframe.

I've used ksvm before but never had this problem.

Here's my sample code:



install.packages("kernlab")
library(kernlab)


set.seed(3233)


trainIndex <-  
caret::createDataPartition(dataset_labeled_fraud$isFraud,  
p=0.70,kist=FALSE)

train <- dataset_labeled_fraud[trainIndex,]
test <- dataset_labeled_fraud[-trainIndex,]


#clear out the training model
filter <- NULL

filter <-  
kernlab::ksvm(isFraud~.,data=train,kernel="rbfdot",kpar=list(sigma=0.5),C=3,prob.model=TRUE)


#clear out the test results
test_pred_rbfdot <- NULL

test_pred_rbfdot <-
kernlab::predict(filter,test,type="probabilities")

dataframe_test_pred_rbfdot <- as.data.frame(test_pred_rbfdot)


nrow(dataframe_test_pred_rbfdot)
> 23300
nrow(test)
> 24408

# ok, how did I go from 24408 input rows to only 23300 output  
prediction rows? :(


Thanks in advance anyone!




Thomas A. Woolman
PhD Candidate, Technology Management
Indiana State University

forgot to mention, the training and testing dataframes are composed of  
4 IVs (one double numeric IV and three factor IVs) and one DV  
(dichotomous factor, i.e. true or false).

The training dataframe consists of 48819 rows and test dataframe  
consists of 24408 rows.



Thanks again.



Quoting Tom Woolman <twoolman at ontargettek.com>:
> Hi everyone. I'm using the kernlab ksvm function with the rbfdot  
> kernel for a binary classification problem and getting a strange  
> result back. The predictions seem to be very accurate judging by the  
> training results provided by the algorithm, but I'm unable to  
> generate a confusion matrix because there is a difference in the  
> number of output records from my model test compared to what was  
> input into the test dataframe.
>
> I've used ksvm before but never had this problem.
>
> Here's my sample code:
>
>
>
> install.packages("kernlab")
> library(kernlab)
>
>
> set.seed(3233)
>
>
> trainIndex <-  
> caret::createDataPartition(dataset_labeled_fraud$isFraud,  
> p=0.70,kist=FALSE)
>
> train <- dataset_labeled_fraud[trainIndex,]
> test <- dataset_labeled_fraud[-trainIndex,]
>
>
> #clear out the training model
> filter <- NULL
>
> filter <-  
>
kernlab::ksvm(isFraud~.,data=train,kernel="rbfdot",kpar=list(sigma=0.5),C=3,prob.model=TRUE)
>
>
> #clear out the test results
> test_pred_rbfdot <- NULL
>
> test_pred_rbfdot <-
kernlab::predict(filter,test,type="probabilities")
>
> dataframe_test_pred_rbfdot <- as.data.frame(test_pred_rbfdot)
>
>
> nrow(dataframe_test_pred_rbfdot)
>
>> 23300
>
> nrow(test)
>
>> 24408
>
>
> # ok, how did I go from 24408 input rows to only 23300 output  
> prediction rows? :(
>
>
> Thanks in advance anyone!
>
>
>
>
> Thomas A. Woolman
> PhD Candidate, Technology Management
> Indiana State University