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Grades of supervision
The learning process supports gradual steps in the realm of supervision:
- Unsupervised Learning doesn't have previous knowledge of the class or value of any sample, it should infer it automatically.
- Semi-Supervised Learning, needs a seed of known samples, and the model infers the remaining samples class or value from that seed.
- Supervised Learning: This approach normally includes a set of known samples, called training set, another set used to validate the model's generalization, and a third one, called test set, which is used after the training process to have an independent number of samples outside of the training set, and warranty independence of testing.
In the following diagram, depicts the mentioned approaches:
Graphical depiction of the training techniques for Unsupervised, Semi-Supervised and Supervised Learning