This article describes the types of Machine Learning problems that the AI & Analytics Engine can tackle.
The AI & Analytics Engine currently supports three types of Machine Learning problems, namely classification, regression, and clustering.
Classification (supervised learning)
Given an observation, predict which category it belongs to, from a set of categories.
Examples:
-
Binary classification -- Is an email spam or not (yes/no)?
-
Multi-class classification -- Which genre does a movie belong to?
Regression (supervised learning)
Given an observation, predict a numeric outcome.
Examples:
- Predict the price of a house
- Predict the duration of a taxi trip
For a detailed comparison between classification and regression, read difference between classification and regression.
Clustering (unsupervised learning)
Partition data in the way that similar entities are grouped into the same cluster.
Examples:
-
Segment customers into groups of similar characteristics to target
-
Group items in a search engine and show similar results for a query
To learn more about typical methods and applications of clustering, read 5 clustering methods and applications.