Overview
Semi-supervised learning falls between supervised and unsupervised learning. It is particularly useful when labeling data is expensive or time-consuming, but unlabeled data is abundant.
How it Works
The model uses the small labeled dataset to learn the basic structure of the data and then applies that knowledge to the larger unlabeled dataset to improve its performance.
Benefits
- Reduces the need for expensive manual labeling.
- Often achieves higher accuracy than purely unsupervised learning.
- More practical for real-world applications with limited labeled data.