en:mathematics:data-analysis:classification:start

Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.

Classification is the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (data objects whose class label is known). The derived model can take the form of classification (IF-THEN) rules, decision trees, mathematical formulae, or neural networks.

- A decision tree is a flow-chart-like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and tree leaves represent classes or class distributions. Decision trees can easily be converted to classification rules.
- A neural network, when used for classification, is a collection of neuron-like processing units with weighted connections between the units.
- There are many other methods for constructing classification models, such as support vector machines, Naïve Bayesian Classification, and Nearest Neighbour Classification.

en/mathematics/data-analysis/classification/start.txt · Last modified: 2019/09/08 15:51 by Digital Dot