Examples of DecisionTreeModel

    int maxBins = 100;
    Strategy strategy = new Strategy(Algo.Classification(), Gini.instance(), maxDepth, numClasses,
        maxBins, categoricalFeaturesInfo);


    DecisionTree learner = new DecisionTree(strategy);
    DecisionTreeModel model = learner.train(rdd.rdd());


    int numCorrect = validatePrediction(arr, model);
    Assert.assertTrue(numCorrect == rdd.count());
  }

Examples of org.apache.spark.mllib.tree.model.DecisionTreeModel

    int numClasses = 2;
    int maxBins = 100;
    Strategy strategy = new Strategy(Algo.Classification(), Gini.instance(), maxDepth, numClasses,
        maxBins, categoricalFeaturesInfo);


    DecisionTreeModel model = DecisionTree$.MODULE$.train(rdd.rdd(), strategy);


    int numCorrect = validatePrediction(arr, model);
    Assert.assertTrue(numCorrect == rdd.count());
  }

Examples of org.apache.spark.mllib.tree.model.DecisionTreeModel

    String impurity = "gini";
    Integer maxDepth = 5;
    Integer maxBins = 32;


    // Train a DecisionTree model for classification.
    final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
      categoricalFeaturesInfo, impurity, maxDepth, maxBins);


    // Evaluate model on training instances and compute training error
    JavaPairRDD<Double, Double> predictionAndLabel =
      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
        }
      });
    Double trainErr =
      1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
        @Override public Boolean call(Tuple2<Double, Double> pl) {
          return !pl._1().equals(pl._2());
        }
      }).count() / data.count();
    System.out.println("Training error: " + trainErr);
    System.out.println("Learned classification tree model:\n" + model);


    // Train a DecisionTree model for regression.
    impurity = "variance";
    final DecisionTreeModel regressionModel = DecisionTree.trainRegressor(data,
        categoricalFeaturesInfo, impurity, maxDepth, maxBins);


    // Evaluate model on training instances and compute training error
    JavaPairRDD<Double, Double> regressorPredictionAndLabel =
      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
          return new Tuple2<Double, Double>(regressionModel.predict(p.features()), p.label());
        }
      });
    Double trainMSE =
      regressorPredictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
        @Override public Double call(Tuple2<Double, Double> pl) {