I have 5 broad metagenomic "ecoregion" categories (just think lots of DNA at different nice locations) which become the training targets for their complete (and augmented) metagenomic data. Any standard model works fine notably random forest, naive Bayes, SVM, confusion matrix is okay and ROC fine. These are small data sets of 10E5 to 10E6.
The categories are very broad and most predictions (meta genomic data from other "ecoregions"), will fall between these categories. ML in contrast will 'relocate' the prediction into 1 of 5 categories. So thus if I've a "woodland" category and a "lake" category, a marsh will fall "in between" the trained classification, but ML will call it either a 'wood' or a 'lake'.
How do I attain that in-between classification status via ML?