Summary

In this chapter, we tapped into a very powerful but lesser known paradigm in machine learning—semi-supervised learning. We began by exploring the underlying concepts of transductive learning and self-training, and improved our understanding of the latter class of techniques by working with a naïve self-training implementation.

We quickly began to see weaknesses in self-training and looked for an effective solution, which we found in the form of CPLE. CPLE is a very elegant and highly applicable framework for semi-supervised learning that makes no assumptions beyond those of the classifier that it uses as a base model. In return, we found CPLE to consistently offer performance in excess of naïve semi-supervised and supervised implementations, ...

Get Python: Deeper Insights into Machine Learning now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.