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.

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.