Is the number of the predicted values correct from the test set for SVM?

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. 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?

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

#ftID <- cbind(ft, seq.int(nrow(ft))
#colnames(ftID)[24]<- "RowID"
#ftIDtestrows <- ftID[-train_ind,24]

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

#Use the predictions on the data
# ---------------- This is where the question is ---------------- #
pred <- predict(model_svm, test)
# ----------------------------------------------------------------#

print(confusionMatrix(pred[1:nrow(test)],testlabel))

#ROC and AUC curves and their plots
#-----------------also------------->  was trying to get this to work as pred doesn't naturally end up with the expected 55 nrow from test set
roc.multi<-multiclass.roc(testlabel, as.numeric(pred[1:55])) 
rs <- roc.multi[['rocs']]
plot.roc(rs[[1]])
sapply(2:length(rs),function(i) lines.roc(rs[[i]],col=i)) ```


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