In earlier posts on our blog, we discussed various types of machine learning, including supervised and unsupervised learning. Today, we continue the series by exploring one of the most versatile and widely used machine learning techniques-hybrid, or semi-supervised learning.
In this article, you will learn about how semi-supervised learning works, what its benefits are, and what algorithms to use.
The definition of semi-supervised learning
As you might already know from our previous articles about supervised and unsupervised learning, a machine learning model can use labeled or unlabeled data for training. Supervised learning uses labeled data, while unsupervised learning employs unlabeled data.
Labeled data helps achieve maximum accuracy but it isn’t always possible to have a sufficiently large dataset of manually prepared data. Semi-supervised, or hybrid, learning is a machine learning technique that combines the use of labeled and unlabeled data for training to enhance model performance.
