c4.5_with_random_forest v0.1.0

C4.5 with random forest: This project is a fork from learningjs, adding the support for random forests

#Data format Input files need to be in CSV-format with 1st line being feature names. One of the features has to be called 'label'. E.g.

There's also an optional 2nd line for feature types and the 'label' column for 2nd line has to be called 'feature_type'. This is useful if feature types are mixed. For Logistic Regression, all features should be real numbers. E.g.

#Usage Data loading: data_util.js provides three methods:

• loadTextFile: the csv-format file will be loaded from disk and columns are parsed as strings unless 2nd line specifies feature types.
• loadRealFile: the csv-format file will be loaded from disk and columns are parsed as real numbers.
• loadString: a big string will be chopped into lines and columns are parsed as strings unless 2nd line specifies feature types.

In the loading callback function you will obtain a data object D on which you can apply the learning methods. Note that only Decision Tree supports both real and categorical features. Logistic Regression works on real features only.

var learningjs = require('learningjs.js');
var data_util = require('data_util.js');
var tree = new learningjs.tree();
data_util.loadRealFile(fn_csv, function(D) {

  //normalize data
  data_util.normalize(D.data, D.nfeatures);

  //logistic regression. following params are optional
  D.optimizer = 'sgd'; //default choice. other choice is 'gd'
  D.learning_rate = 0.005;
  D.l2_weight = 0.0;
  D.iterations = 1000; //increase number of iterations for better performance

  new learningjs.train(D, function(model, err){
    if(err) {
      console.log(err);
    } else {
      model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
        console.log('training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
      });
      data_util.loadRealFile(fn_test, function(T) {
        model.calcAccuracy(T.data, T.targets, function(acc, correct, total){
          console.log('    test: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
        });
      });
    }
  });

  var randomForest = new learningjs.randomForest();
  var treesCount = 10;
  randomForest.train(D, treesCount, function(model, err) {
      if (err) {
          console.log(err);
      } else {
          model.calcAccuracy(D.data, D.targets, function(firstInAcc, existInAcc, correct, exist, total){
              console.log('training : ');
              console.log('  exists in randomForest results:', exist, '/', total, '=', (existInAcc*100.0).toFixed(2)+'%');
              console.log('  first in randomForest results:', correct, '/', total, '=', +(firstInAcc*100.0).toFixed(2)+'%');
          });
          model.calcAccuracy(T.data, T.targets, function(firstInAcc, existInAcc, correct, exist, total) {
              console.log('test : ');
              console.log('  exists in randomForest results:', exist, '/', total, '=', (existInAcc*100.0).toFixed(2)+'%');
              console.log('  first in randomForest results:', correct, '/', total, '=', +(firstInAcc*100.0).toFixed(2)+'%');
          });
      }
  });
});

#Documentation See learningjs, which is the original project for more information and for demo.

#License MIT