Semi-supervised learning combines which two types of learning?

Semi-supervised learning indeed combines supervised and unsupervised learning. In supervised learning, a model is trained on a labeled dataset, meaning that each training example is paired with an output label. This method requires a substantial amount of labeled data, which can be costly and time-consuming to collect.

In contrast, unsupervised learning utilizes unlabelled data to identify patterns and structures within the data itself, without the need for predefined outputs. This approach is advantageous when labeled data is scarce or difficult to obtain.

Semi-supervised learning effectively marries these two approaches by using a small amount of labeled data alongside a larger volume of unlabeled data. This hybrid methodology enhances the learning process, allowing models to improve their predictive capabilities by leveraging the structure found in the unlabeled data while still being guided by the labeled data.

This combination leads to more robust models, especially in scenarios where acquiring labeled data is expensive or infeasible, making semi-supervised learning a valuable technique in the realm of machine learning.