Initial results were less satisfying and most errors occurred because mono-varietals were predicted to be blends and vice versa.
we explored two solutions: (1) we separated our data into mono-varietals (Model 1), and blends (Model 2), and trained HSLDAd separately; (2) we created a smaller yet more balanced training set regarding mono-varietals and blends (Model 3).