Tuning svm and cart hyperparameters
There's two common approaches here, depending on how much data you have.

If you have plenty of data, you'll probably be fine to:


set part of the training set aside, and call it your validation set;
start with a set of hyperparameters, train a model on your training set, evaluate performance on the validation set;
repeat step 2 with different hyperparameters;
pick the hyperparameters which give you the best score on the validation set;
train your model on the training set and the validation set;
Test your model ONCE on your test set.


Otherwise, you can do the following:


start with a set of hyperparameters, evaluate your model's performance on unseen data via cross-validation on the training set;
repeat step 2 with different hyperparameters;
pick the hyperparameters which give you the best score on the validation set;
train your model on the entire training set;
Test your model ONCE on your test set.