Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. This means some data is already tagged with the correct answer.
Examples of supervised machine learning include:
- Classification, identifying input data as part of a learned group.
- Regression, predicting outcomes from continuously changing data.
Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.
Examples of unsupervised machine learning include:
- Clustering, grouping together data points with similar data.
- Association, understanding how certain data features connect with other features.
The main differences of supervised vs unsupervised learning include:
- The need for labeled data in supervised machine learning.
- The problem the model is deployed to solve. Supervised machine learning is generally used to classify data or make predictions, whereas unsupervised learning is generally used to understand relationships within datasets.
- Supervised machine learning is much more resource-intensive because of the need for labeled data.
- In unsupervised machine learning, it can be more difficult to reach adequate levels of explainability because of less human oversight.