I am doing regression analysis on a data set with over 20000 samples using scikit learn. Trying to use regression models to fit three features to label which is a score ranges from 0 to 10. Problem is only 100 of the data has a known score. The rest are all unlabeled.
Semi-supervised learning seems to work well with classification problems using methods like label-propagation. I wonder if it works for regression problems as well. If so, where can I find any examples for labeling unlabeled data based on similarity.