I have gone through the following 2 steps to create a 4-class SVM model:
5 fold cross validation with grid search to find a C and Gamma value with the lowest error.
Train a model with my complete data set using the C and Gamma values found above.
And now for the 3rd step, I am not sure what the best way is to report the predictive accuracy of my model. One thing which confuses me is the 'Cross Validation accuracy (90%)' reported in Step 1, what is this and I can't report this as my classification accuracy right?
The two metrics I am considering reporting are True detection (Accuracy), and a confusion matrix.
My data consists of four classes, each with only 10 samples, and each sample has 264 features.
I know it is very little data, I could possibly measure another 20 samples for each class over two days, but would rather explore how far I can take my current set first.