Interview question: What distinguishes supervised learning from unsupervised learning?
Both supervised learning and unsupervised learning are branches of machine learning.
What distinguishes supervised learning from unsupervised learning is the fact that supervised learning is labelled, but unsupervised learning is not.
The label is what is being predicted on, such as a type of iris, whether a person has cancer, or the price of a house.
Supervised learning problems are not labelled and are therefore clustered, which is the term used to group examples of grouping unlabelled examples.
In addition, the algorithms used for supervised learning are different from those used for unsupervised learning.
Some examples of some machine learning algorithms used for supervised learning are:-
- Logistic regression
- Linear regression
- Decision tree
- Random forest
- K nearest neighbour
- Support vector machine
- Naive Bayes
Some examples of machine learning algorithms used for unsupervised learning are:-
- K means clustering
- Affinity propagation
