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Supervised Learning and Classification

Problem: Given multivariate data, i.e. a number of things, drawn from a sample space T×U, learn a function in T->U, i.e. function to predict the output (dependent, endogenous) attributes in U, given the input (independent, exogenous) attributes in T.

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   and contains hyper-links to other resources.

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Examples of supervised learning

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Some terms

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.

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Supervised Classification

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:
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Now see decision trees

© L. Allison, School of Computer Science and Software Engineering, Monash University, Australia 3800.
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