Problem:
Given multivariate data, i.e. a number of things,
drawn from a sample space T×U,
learn a function in T->U,
Called "supervised" because examples are given (e.g. by a supervisor or expert) and the aim is to infer a function to mimic the given behaviour.
Supervised Classification is a special case of the above where U is a discrete attribute often called "the class".
This document is online at http://www.csse.monash.edu.au/~lloyd/Archive/2005-07-Supervised/index.shtml and contains hyper-links to other resources.
Training Set: A data set on which to learn the model.
Test Set: A data set given after learning, to test the accuracy of predictions.
Overfitting: When a too complex model is learned - often revealed when errors on the test set are much worse than those on the training set.
Supervised classification is a special case of supervised learning where the output-space, U, is a single discrete attribute often called "the class".
(cf Unsupervised classification where the number of classes, their spec's and memberships are all unknown.)
Example model classes:Now see decision trees