Logistic

 SparkConf configuration = new SparkConf().setAppName("logisApp");
                    SparkContext sparkContext = new SparkContext(configuration);
                    Logger rootLogger = Logger.getRootLogger();
                    rootLogger.setLevel(Level.ERROR);


                    JavaRDD<LabeledPoint> allData = dataHelper.readTextFromfile(sparkContext, "path/to/all/data");;

                    // Split initial RDD into two... [60% training data, 40%
                    // testing data].

                    JavaRDD<LabeledPoint>[] parts = allData.randomSplit(new double[] { 0.6, 0.4 }, 11L);
                    JavaRDD<LabeledPoint> trainingData = parts[0];
                    JavaRDD<LabeledPoint> valdidationData = parts[1];

                    //
                    // Run training algorithm to build the model.
                    LogisticRegressionWithLBFGS logisticRegression = new LogisticRegressionWithLBFGS().setNumClasses(2);
                    logisticRegression.optimizer().setUpdater(new L1Updater());
                    final LogisticRegressionModel model = logisticRegression.run(trainingData.rdd());


                    //
                    // Compute raw scores on the test set.
                    JavaRDD<Tuple2<Object, Object>> predict = valdidationData
                            .map(new Function<LabeledPoint, Tuple2<Object, Object>>() {
                                public Tuple2<Object, Object> call(LabeledPoint point) {
                                    Double prediction = model.predict(point.features());
                                    return new Tuple2<Object, Object>(prediction, point.label());
                                }
                            });
                    //
                    // Get evaluation metrics.
                    BinaryClassificationMetrics metrics = new BinaryClassificationMetrics(predict.rdd());

                    // Precision
                    JavaRDD<Tuple2<Object, Object>> precision = metrics.precisionByThreshold().toJavaRDD();

                    // F Score
                    JavaRDD<Tuple2<Object, Object>> f1Score = metrics.fMeasureByThreshold().toJavaRDD();
                    System.out.println("    F1 Score : " + f1Score.collect());