I am working with a typical ML problem, trying to separate 2 classes in a supervised manner. I wanted to ask at which point you decide the data is not descriptive enough to solve the problem, and if there is anything furhter you can do.
My problem is that ive been given throught my education may tools to solve problems, and improve on existing algorithms, but not told what to do when the data doesnt provide enough variance.
The data set is described by around 20 continuous feautres with binary labels for each case. I have used T-SNE (seen below) to visualise a dimensionality reduced version of my data set.
From this, and PCA plots I have made, I would say the data is certainly not linearly separable, and not non-linearly separable either.
What steps can I take to help separate the classes? Will combining features aid this process (ratios between two etc) or is my understanding correct that these relationships will be found if important.
Any advise would be appreciated.
EDIT: PCA PLOT