I have been using CP/CPA for some time now, but I’m still missing a piece of information about how the classification model works. If I understood well, I can save a classifier model and apply it on a new data set by the mean of “load classifier model”, only if I’m using Fast Gentle Boosting algorithm, is ir right? I’m often using Random Forest as it performs better, but I’m unable to apply the model generated from this method to a new set of images. Moreover, It’s not clear to me if the .model generated only comprises classification rules…or something else…
I often have a big batch problem with my images especially if I collect pictures in fluorescence, as fluorescence decay or other experimental flaws are often detrimental for a good reproducibility. So I must re-run the classification process based on a new training set for each sample But if I’m able to perfectly standardize the staining method, I will surely apply the rules of my first classification on all my samples. This “forecasting” method could be really useful and implemented.
So, my question is: is it true that it’s not possible to apply saved rules on new images sets? Is there any way to implement this new function?
I read some posts about this topic but it seems there has been no updates about it
Thank you very much for this beautiful software