What is Semi-Supervised Learning? A Guide for Beginners.

Semi-supervised learning (SSL) is a machine learning technique that combines elements of supervised and unsupervised learning to train models using a small set of labeled data along with a large amount of unlabeled data, addressing the time and cost constraints of manual data labeling. This approach is beneficial for various tasks, including classification, regression, clustering, and anomaly detection, by leveraging assumptions like continuity, cluster, and manifold to find relationships within the dataset. Techniques such as pseudo-labeling, self-training, and label propagation are employed to iteratively refine model predictions by utilizing the confidence of predictions from the partially trained model. Although effective in many applications, SSL requires representative labeled data to perform well, and it is not suitable for all tasks, especially when the labeled data does not represent the full distribution of the dataset.