The most common classes of problems in Machine Learning are -

Classification

• In classification, data is labeled i.e., it is assigned a class, for example, spam/non-spam or fraud/non-fraud.
• The decision being modeled is to assign labels to new unlabeled pieces of data.
• This can be thought of as a discrimination problem, modeling the differences or similarities between groups.

Regression

*Regression data is labeled with real value rather than a categorical label.
*The decision being modeled is what value to predict for new unpredicted data.

Clustering

• In clustering, data is not labeled but can be divided into groups based on similarity and other measures of natural structure in the data.
• For example, organizing pictures by faces without names, where the human user has to assign names to groups, like iPhoto on the Mac.

Rule Extraction

• In rule extraction, data is used as the basis for the extraction of propositional rules.
• These rules discover statistically supportable relationships between attributes in the data.

This tutorial is originally published at - https://www.asquero.com/article/classes-of-problems-in-machine-learning/

Also Read -

Splitting Dataset in Machine Learning - https://www.asquero.com/article/splitting-dataset-in-machine-learning

Terminologies used in Machine Learning - https://www.asquero.com/article/terminologies-used-in-machine-learning

Classes of Problems in Machine Learning - https://www.asquero.com/article/classes-of-problems-in-machine-learning