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?

  • $\begingroup$ If the scores are only integers from 0 to 10, did you try semi-supervised classification on this data? $\endgroup$
    – rnso
    Nov 4, 2018 at 2:10

1 Answer 1


I also had a similar dataset, I came across covarite shift technique of Machine Learning. As you have changes in the distribution of the input variables in training data. Though the predictions might also be wrong using this.


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