The most common classes of problems in Machine Learning are -
- 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 data is labeled with real value rather than a categorical label.
*The decision being modeled is what value to predict for new unpredicted data.
- 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.
- 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