Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

by Chip Huyen

Semi-supervised learning is a technique that was used back in the 90s,16 and since then many semi-supervision methods have been developed. A comprehensive review of semi-supervised learning is out of the scope of this book. We’ll go over a small subset of these methods to give readers a sense of how they are used. For a comprehensive review, I recommend “Semi-Supervised Learning Literature Survey” (Xiaojin Zhu, 2008) and “A Survey on Semi-Supervised Learning” (Engelen and Hoos, 2018). A classic semi-supervision method is self-training. You start by training a model on your existing set of labeled data and use this model to make predictions for unlabeled samples. Assuming that predictions with high raw probability scores are correct, you add the labels predicted with high probability to your training set and train a new model on this expanded training set. This goes on until you’re happy with your model performance.