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
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