Why does SVM work when using the comma delimited form but not the formula form? R

So I have a data set of nrow = 218, and I'm going through [this][https://iamnagdev.com/2018/01/02/sound-analytics-in-r-for-animal-sound-classification-using-vector-machine/] example [git here][https://github.com/nagdevAmruthnath]. I've split my data into train (nrow = 163; ~75%) and test (nrow = 55; ~25%).

When I get to the part where "pred <- predict(model_svm, test)", if I convert pred into a data frame, instead of 55 rows there are 163 (when using the function form of the svm call). Is this normal because it used 163 rows to train? Or should it only have 55 rows since Im using the test set to test?

When I use the 'formula' form of the svm I have issues with the # of rows in the predict function:

model_svm <- svm(trainlabel ~ as.matrix(train) )

But when I use the 'traditional' form, predict on the test data works fine:

model_svm <- svm(as.matrix(train), trainlabel)

Any idea why this is?

Some fake data:

featuredata_all <- matrix(rexp(218, rate=.1), ncol=23)

Some of the code:

library(data.table)

pt1 <- scale(featuredata_all[,1:22],center=T)
pt2 <- as.character(featuredata_all[,23]) #since the label is a string I kept it separate 

ft<-cbind.data.frame(pt1,pt2) #to preserve the label in text
colnames(ft)[23]<- "Cluster"

## 75% of the sample size
smp_size <- floor(0.75 * nrow(ft))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(ft)), size = smp_size)

train <- ft[train_ind,1:22] #163 reads
test  <- ft[-train_ind,1:22] #55 reads

trainlabel<- ft[train_ind,23] #163 labels
testlabel <- ft[-train_ind,23] #55 labels

#Support Vector Machine for classification
model_svm <- svm(trainlabel ~ as.matrix(train) )
summary(model_svm)

#Use the predictions on the data
pred <- predict(model_svm, test) 


 [1]: https://iamnagdev.com/2018/01/02/sound-analytics-in-r-for-animal-sound-classification-using-vector-machine/
 [2]: https://github.com/nagdevAmruthnath