Motivated from #43, if the aim is to generalize the predictions to a second target:
The user submits two (or several) targets. One of them will be used to fit the statistical model. The others will be simply correlated with the reweighted predicting matrix, essentially generalizing the model. This should likely be done in the func fit_and_score.
This will take the reweighted predicting matrix as it was receveived when fitting to target and generalize it to another target. Basically, the question this would answer is: if I reweight my predictor with regards to target, how well does the reweighted predicting matrix now relate to another target for the same images? Therefore, this does not allow generalizing the model to a matrix with (dis-)similarities from different images (for that see #47).
Motivated from #43, if the aim is to generalize the predictions to a second target:
The user submits two (or several) targets. One of them will be used to fit the statistical model. The others will be simply correlated with the reweighted predicting matrix, essentially generalizing the model. This should likely be done in the func
fit_and_score.This will take the reweighted predicting matrix as it was receveived when fitting to
targetand generalize it to another target. Basically, the question this would answer is: if I reweight mypredictorwith regards totarget, how well does the reweighted predicting matrix now relate to another target for the same images? Therefore, this does not allow generalizing the model to a matrix with (dis-)similarities from different images (for that see #47).