I am relatively new in Machine Learning. I am using
Random Forest and
SVM for a project. Where I did a
10-fold cross-validation on the train set. which gives the following K-Fold Average Score:
Random Forest: 0.8716 & SVM: 0.8665
On the other hand, when I tested with the independent test set it gives the following accuracy:
Random Forest: 93.63% SVM: 90.47%
I am confused is it ok? I mean can the test accuracy be greater than K-fold? Is it what underfitting called. Please help. TIA.