/** * Main method for this class. * * @param argv the options */ public static void main(String[] argv) { runClassifier(new RandomForest(), argv); } }
/** * Main method for this class. * * @param argv the options */ public static void main(String[] argv) { runClassifier(new RandomForest(), argv); } }
/** Creates a default RandomForest */ public Classifier getClassifier() { return new RandomForest(); }
/** Creates a default RandomForest */ public Classifier getClassifier() { return new RandomForest(); }
Classifier Clfs = null; try { if (modelType.equals("J48")) { Clfs = new J48(); } else if (modelType.equals("MLP")) { Clfs = new MultilayerPerceptron(); } else if (modelType.equals("IB3")) { Clfs = new IBk(3); } else if (modelType.equals("RF")) { Clfs = new RandomForest(); } else if (modelType.equals("NB")) { Clfs = new NaiveBayes(); //...
trainData.setClassIndex(trainData.numAttributes() - 1); RandomForest rf = new RandomForest(); rf.setNumTrees(50);
Classifier cls = new RandomForest(); cls.buildClassifier(train);
classifiers[i] = new weka.classifiers.trees.RandomForest(); classifiers[i].setOptions(new String[]{"-I", args[1], "-num-slots", Integer.toString(threadNums.get(i)) });
classifiers[i] = new weka.classifiers.trees.RandomForest(); classifiers[i].setOptions(new String[]{"-I", args[1], "-num-slots", Integer.toString(threadNums.get(i)) });