I'm performing a binary sentiment classification (positive/negative) based on a Naive Bayes classificator and a SVM. To select top k features I use the MRMR algorithm. The model is trained using a 10-fold cross validation and then tested on unseen data.
As you can see, I plotted the top k features from 25 to 350 and the accuracy as well as the f-measure since the algorithms have trouble to predict positive classes. Who can help me to understand them?
Why do they converge and diverge? And what is the "best" model (main criterion is f-measure) and the best size of k according to the graphs?