Classification is the process of finding a model 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 may be represented in various forms, such as classification (IF-THEN) rules, decision trees, or neural network.
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.
A neural network, when used for classification, is typically a collection of neuron like processing units with weighted connection between the units.
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.
A neural network, when used for classification, is typically a collection of neuron like processing units with weighted connection between the units.
Example 4
IF-THEN rules,
Student (class, "undergraduate") AND concentration (level, "high") ==> class A
Student (class,"undergraduate") AND concentration (level,"low") ==> class B
Student (class, "post graduate") ==> class C
Neural network. Decision tree
