Which method would you typically use to train a model on unlabelled data?

Unsupervised learning is the most appropriate method for training a model on unlabelled data. In this approach, the model analyzes data without any predefined categories or outcomes. The goal is often to identify patterns, groupings, or underlying structures within the data. For instance, unsupervised learning can involve clustering, where the model finds natural groupings within the data, or association, where it discovers rules that describe large portions of the data.

Unlike supervised learning, which requires labeled data for training and involves teaching the model with input-output pairs, unsupervised learning thrives in situations where labels are not available. Reinforcement learning, while capable of making decisions based on rewards and penalties, also does not directly apply to unlabelled data, as it typically requires a clear feedback mechanism involving defined states and actions. Deep learning, as a subset of machine learning, can be used in both supervised and unsupervised domains, but the method chosen to process unlabelled data is usually unsupervised learning.