why SVM predicted rows more than the RF and Decision tree


I run a prediction model and after dividing the data into 2 parts, training and testing, I run 3 different algorithms.
After running the SVM, RF and decision tree classification algorithm, when I look at the confusion matrix and the Predictor node of each algorithm, the number of tested rows is the same for RF and decision tree but more for SVM. What is the reason. I would appreciate if someone can explain.
Thank you.

and also I have taken warning for SVM learner like: " Rejecting 1 column(s) due to incompatible type: [Document]"

so what can be the reason, how can I fix this message issue

You could try and remove data types document. Also you could use the

In order to determine the type and content of your data. Just to be sure.