/DecisionTreeGenerator

Generates and visualizes a decision tree model using a training data set by using the ID3 algorithm. Able to test accuracy of the model using test data set. Pruning and gain ratio feature included.

Primary LanguagePython

DecisionTreeGenerator

This application can develop a decision tree from a training data set containing categorical attributes and a class label. In the example below, Outlook, Temperature, Humidity, and Wind are the categorical attributes (inputs). PlayTennis is the class label (output). The objective is to create a decision tree so we can make predictions on what the class label (output) will be, given a set of categorical attribute values (inputs)

The decision tree below was modeled using the ID3 algorithm.