I have a set of relatively long ($\sim 1000$) binary features with scalar values $[0-10]$ attached to them. My aim is to write a predictor that learns to map the features to the $[0-10]$ interval to predict new features when given a new binary vector. I used SVM and Lasso with leave-one-out performance analysis, but both always end up predicting the mean value of the distribution (correlates to the histogram of all the feature - scalar distribution). The histograms are also rather norm / Rayleigh distributions. Suggestions for algorithms / feature space mapping? My main problem is that I am dealing with binary features for the first time.