From the preceding results, it can be seen that LR and SVM models have the same but higher false positive rate compared to Random Forest and DT. So we can say that DT and Random Forest have better accuracy overall in terms of true positive counts. Let's see the validity of the preceding statement with prediction distributions on pie charts for each model:
Now, it's worth mentioning that using random forest, we are actually getting high accuracy, but it's a very resource, as well as time-consuming job; the training, especially, takes a considerably longer time as compared to LR and SVM.
Therefore, if you don't have higher memory or computing power, it is recommended to increase the Java heap space prior to running this code to avoid OOM errors.
Finally, if you want to deploy the best model (that is, Random Forest in our case), it is recommended to save the cross-validated model immediately after the fit() method invocation:
// Save the workflow cvModel...