Machine Learning Lesson of the Day – Supervised and Unsupervised Learning
January 4, 2014 Leave a comment
The 2 most commonly used and studied categories of machine learning are supervised learning and unsupervised learning.
- In supervised learning, there is a target variable, , and a set of predictor variables, . The goal is to use to predict . Supervised learning is synonymous with predictive modelling, but the latter term does not connote with learning from data to improve performance in future prediction. Nonetheless, when I explain supervised learning to people who have some background in statistics or analytics, they usually understand what I mean when I tell them that it is just predictive modelling.
- In unsupervised learning, there are only predictor variables and no target variable. The goal is to find interesting patterns in . This is a much less concretely defined problem than supervised learning. Unsupervised learning is sometimes called pattern discovery, pattern recognition, or knowledge discovery, though these are not commonly agreed upon synonyms.