Small JavaScript implementation of algorithm for training Decision Tree and Random Forest classifiers.
This project is made possible by the ID3 implementation efforts of lagodiuk (whom I've forked this project from).
This project extends the original decision tree generation algorithm by providing a results-oriented forecasting prediction model. Data is inputted by hand as a method of the cleaning/preparation component of the data mining workflow and then processed with extra elements on top of simple prediction.
A set of student data S1 is given to the model as training data. Follow-on data in a set S2 is presented and then predictions of S2 are created from the trained model using S1. Individual predictions are made and then an aggregate model accuracy is provided. (under construction)
###Example of usage### Predicting sex of characters from 'The Simpsons' cartoon, using such features as weight, hair length and age
Online demo: http://jsfiddle.net/xur98/
// Training set
var data =
[{person: 'Homer', hairLength: 0, weight: 250, age: 36, sex: 'male'},
{person: 'Marge', hairLength: 10, weight: 150, age: 34, sex: 'female'},
{person: 'Bart', hairLength: 2, weight: 90, age: 10, sex: 'male'},
{person: 'Lisa', hairLength: 6, weight: 78, age: 8, sex: 'female'},
{person: 'Maggie', hairLength: 4, weight: 20, age: 1, sex: 'female'},
{person: 'Abe', hairLength: 1, weight: 170, age: 70, sex: 'male'},
{person: 'Selma', hairLength: 8, weight: 160, age: 41, sex: 'female'},
{person: 'Otto', hairLength: 10, weight: 180, age: 38, sex: 'male'},
{person: 'Krusty', hairLength: 6, weight: 200, age: 45, sex: 'male'}];
// Configuration
var config = {
trainingSet: data,
categoryAttr: 'sex',
ignoredAttributes: ['person']
};
// Building Decision Tree
var decisionTree = new dt.DecisionTree(config);
// Building Random Forest
var numberOfTrees = 3;
var randomForest = new dt.RandomForest(config, numberOfTrees);
// Testing Decision Tree and Random Forest
var comic = {person: 'Comic guy', hairLength: 8, weight: 290, age: 38};
var decisionTreePrediction = decisionTree.predict(comic);
var randomForestPrediction = randomForest.predict(comic);Data taken from presentation: http://www.cs.sjsu.edu/faculty/lee/cs157b/ID3-AllanNeymark.ppt
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