I have a logical problem when programming my tumor identification algorithm.
In my data sample, I have tested multiple antibodies on tumors - to identify whether those tumors are good or bad. This is essentially a binary classification. Each antibody (respectively "classifier") showed to have a different explanatory / prediction power when used on its own.
My question in regards to the guideline I now need to program is:
Do I get a higher overall prediction power if I use all antibodies on a new tumor? Do classifiers with a worse explanatory still improve my overall prediction? Or would only using the antibody with the "best" individual prediction power lead to a better result?
The resulting algorithm would then, step by step, test each antibody against each new tumor - each test would be done independently.
And I somehow can't wrap my head around what's better and whether I should simply use only the "best" antibody (i.e. with the best predictive power) or use various to improve my prediction result.