content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.apache.spark.examples.ml;
  
  // $example on$
  import org.apache.spark.ml.Pipeline;
  import org.apache.spark.ml.PipelineModel;
  import org.apache.spark.ml.PipelineStage;
  import org.apache.spark.ml.evaluation.RegressionEvaluator;
  import org.apache.spark.ml.feature.VectorIndexer;
  import org.apache.spark.ml.feature.VectorIndexerModel;
  import org.apache.spark.ml.regression.RandomForestRegressionModel;
  import org.apache.spark.ml.regression.RandomForestRegressor;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  // $example off$
  
  public class JavaRandomForestRegressorExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaRandomForestRegressorExample")
        .getOrCreate();
  
      // $example on$
      // Load and parse the data file, converting it to a DataFrame.
      Dataset<Row> data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
  
      // Automatically identify categorical features, and index them.
      // Set maxCategories so features with > 4 distinct values are treated as continuous.
      VectorIndexerModel featureIndexer = new VectorIndexer()
        .setInputCol("features")
        .setOutputCol("indexedFeatures")
        .setMaxCategories(4)
        .fit(data);
  
      // Split the data into training and test sets (30% held out for testing)
      Dataset<Row>[] splits = data.randomSplit(new double[] {0.7, 0.3});
      Dataset<Row> trainingData = splits[0];
      Dataset<Row> testData = splits[1];
  
      // Train a RandomForest model.
      RandomForestRegressor rf = new RandomForestRegressor()
        .setLabelCol("label")
        .setFeaturesCol("indexedFeatures");
  
      // Chain indexer and forest in a Pipeline
      Pipeline pipeline = new Pipeline()
        .setStages(new PipelineStage[] {featureIndexer, rf});
  
      // Train model. This also runs the indexer.
      PipelineModel model = pipeline.fit(trainingData);
  
      // Make predictions.
      Dataset<Row> predictions = model.transform(testData);
  
      // Select example rows to display.
      predictions.select("prediction", "label", "features").show(5);
  
      // Select (prediction, true label) and compute test error
      RegressionEvaluator evaluator = new RegressionEvaluator()
        .setLabelCol("label")
        .setPredictionCol("prediction")
        .setMetricName("rmse");
      double rmse = evaluator.evaluate(predictions);
      System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
  
      RandomForestRegressionModel rfModel = (RandomForestRegressionModel)(model.stages()[1]);
      System.out.println("Learned regression forest model:\n" + rfModel.toDebugString());
      // $example off$
  
      spark.stop();
    }
  }
  