MLlib - Ensembles

An ensemble method is a learning algorithm which creates a model composed of a set of other base models. MLlib supports two major ensemble algorithms: GradientBoostedTrees and RandomForest. Both use decision trees as their base models.

Gradient-Boosted Trees vs. Random Forests

Both Gradient-Boosted Trees (GBTs) and Random Forests are algorithms for learning ensembles of trees, but the training processes are different. There are several practical trade-offs:

In short, both algorithms can be effective, and the choice should be based on the particular dataset.

Random Forests

Random forests are ensembles of decision trees. Random forests are one of the most successful machine learning models for classification and regression. They combine many decision trees in order to reduce the risk of overfitting. Like decision trees, random forests handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions.

MLlib supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features. MLlib implements random forests using the existing decision tree implementation. Please see the decision tree guide for more information on trees.

Basic algorithm

Random forests train a set of decision trees separately, so the training can be done in parallel. The algorithm injects randomness into the training process so that each decision tree is a bit different. Combining the predictions from each tree reduces the variance of the predictions, improving the performance on test data.

Training

The randomness injected into the training process includes:

Apart from these randomizations, decision tree training is done in the same way as for individual decision trees.

Prediction

To make a prediction on a new instance, a random forest must aggregate the predictions from its set of decision trees. This aggregation is done differently for classification and regression.

Classification: Majority vote. Each tree’s prediction is counted as a vote for one class. The label is predicted to be the class which receives the most votes.

Regression: Averaging. Each tree predicts a real value. The label is predicted to be the average of the tree predictions.

Usage tips

We include a few guidelines for using random forests by discussing the various parameters. We omit some decision tree parameters since those are covered in the decision tree guide.

The first two parameters we mention are the most important, and tuning them can often improve performance:

The next two parameters generally do not require tuning. However, they can be tuned to speed up training.

Examples

Classification

The example below demonstrates how to load a LIBSVM data file, parse it as an RDD of LabeledPoint and then perform classification using a Random Forest. The test error is calculated to measure the algorithm accuracy.

import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.util.MLUtils

// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a RandomForest model.
//  Empty categoricalFeaturesInfo indicates all features are continuous.
val numClasses = 2
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 3 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 4
val maxBins = 32

val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
  numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

// Evaluate model on test instances and compute test error
val labelAndPreds = testData.map { point =>
  val prediction = model.predict(point.features)
  (point.label, prediction)
}
val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
println("Test Error = " + testErr)
println("Learned classification forest model:\n" + model.toDebugString)
import scala.Tuple2;
import java.util.HashMap;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.RandomForest;
import org.apache.spark.mllib.tree.model.RandomForestModel;
import org.apache.spark.mllib.util.MLUtils;

SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForestClassification");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

// Load and parse the data file.
String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();
// Split the data into training and test sets (30% held out for testing)
JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
JavaRDD<LabeledPoint> trainingData = splits[0];
JavaRDD<LabeledPoint> testData = splits[1];

// Train a RandomForest model.
//  Empty categoricalFeaturesInfo indicates all features are continuous.
Integer numClasses = 2;
HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
Integer numTrees = 3; // Use more in practice.
String featureSubsetStrategy = "auto"; // Let the algorithm choose.
String impurity = "gini";
Integer maxDepth = 5;
Integer maxBins = 32;
Integer seed = 12345;

final RandomForestModel model = RandomForest.trainClassifier(trainingData, numClasses,
  categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins,
  seed);

// Evaluate model on test instances and compute test error
JavaPairRDD<Double, Double> predictionAndLabel =
  testData.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 testErr =
  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() / testData.count();
System.out.println("Test Error: " + testErr);
System.out.println("Learned classification forest model:\n" + model.toDebugString());
from pyspark.mllib.tree import RandomForest
from pyspark.mllib.util import MLUtils

# Load and parse the data file into an RDD of LabeledPoint.
data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model.
#  Empty categoricalFeaturesInfo indicates all features are continuous.
#  Note: Use larger numTrees in practice.
#  Setting featureSubsetStrategy="auto" lets the algorithm choose.
model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
                                     numTrees=3, featureSubsetStrategy="auto",
                                     impurity='gini', maxDepth=4, maxBins=32)

# Evaluate model on test instances and compute test error
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count())
print('Test Error = ' + str(testErr))
print('Learned classification forest model:')
print(model.toDebugString())

Regression

The example below demonstrates how to load a LIBSVM data file, parse it as an RDD of LabeledPoint and then perform regression using a Random Forest. The Mean Squared Error (MSE) is computed at the end to evaluate goodness of fit.

import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.util.MLUtils

// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a RandomForest model.
//  Empty categoricalFeaturesInfo indicates all features are continuous.
val numClasses = 2
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 3 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "variance"
val maxDepth = 4
val maxBins = 32

val model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo,
  numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

// Evaluate model on test instances and compute test error
val labelsAndPredictions = testData.map { point =>
  val prediction = model.predict(point.features)
  (point.label, prediction)
}
val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
println("Test Mean Squared Error = " + testMSE)
println("Learned regression forest model:\n" + model.toDebugString)
import java.util.HashMap;
import scala.Tuple2;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.RandomForest;
import org.apache.spark.mllib.tree.model.RandomForestModel;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;

SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForest");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

// Load and parse the data file.
String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();
// Split the data into training and test sets (30% held out for testing)
JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
JavaRDD<LabeledPoint> trainingData = splits[0];
JavaRDD<LabeledPoint> testData = splits[1];

// Set parameters.
//  Empty categoricalFeaturesInfo indicates all features are continuous.
Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
String impurity = "variance";
Integer maxDepth = 4;
Integer maxBins = 32;

// Train a RandomForest model.
final RandomForestModel model = RandomForest.trainRegressor(trainingData,
  categoricalFeaturesInfo, impurity, maxDepth, maxBins);

// Evaluate model on test instances and compute test error
JavaPairRDD<Double, Double> predictionAndLabel =
  testData.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 testMSE =
  predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
    @Override
    public Double call(Tuple2<Double, Double> pl) {
      Double diff = pl._1() - pl._2();
      return diff * diff;
    }
  }).reduce(new Function2<Double, Double, Double>() {
    @Override
    public Double call(Double a, Double b) {
      return a + b;
    }
  }) / testData.count();
System.out.println("Test Mean Squared Error: " + testMSE);
System.out.println("Learned regression forest model:\n" + model.toDebugString());
from pyspark.mllib.tree import RandomForest
from pyspark.mllib.util import MLUtils

# Load and parse the data file into an RDD of LabeledPoint.
data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model.
#  Empty categoricalFeaturesInfo indicates all features are continuous.
#  Note: Use larger numTrees in practice.
#  Setting featureSubsetStrategy="auto" lets the algorithm choose.
model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo={},
                                    numTrees=3, featureSubsetStrategy="auto",
                                    impurity='variance', maxDepth=4, maxBins=32)

# Evaluate model on test instances and compute test error
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / float(testData.count())
print('Test Mean Squared Error = ' + str(testMSE))
print('Learned regression forest model:')
print(model.toDebugString())

Gradient-Boosted Trees (GBTs)

Gradient-Boosted Trees (GBTs) are ensembles of decision trees. GBTs iteratively train decision trees in order to minimize a loss function. Like decision trees, GBTs handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions.

MLlib supports GBTs for binary classification and for regression, using both continuous and categorical features. MLlib implements GBTs using the existing decision tree implementation. Please see the decision tree guide for more information on trees.

Note: GBTs do not yet support multiclass classification. For multiclass problems, please use decision trees or Random Forests.

Basic algorithm

Gradient boosting iteratively trains a sequence of decision trees. On each iteration, the algorithm uses the current ensemble to predict the label of each training instance and then compares the prediction with the true label. The dataset is re-labeled to put more emphasis on training instances with poor predictions. Thus, in the next iteration, the decision tree will help correct for previous mistakes.

The specific mechanism for re-labeling instances is defined by a loss function (discussed below). With each iteration, GBTs further reduce this loss function on the training data.

Losses

The table below lists the losses currently supported by GBTs in MLlib. Note that each loss is applicable to one of classification or regression, not both.

Notation: $N$ = number of instances. $y_i$ = label of instance $i$. $x_i$ = features of instance $i$. $F(x_i)$ = model’s predicted label for instance $i$.

LossTaskFormulaDescription
Log Loss Classification $2 \sum_{i=1}^{N} \log(1+\exp(-2 y_i F(x_i)))$Twice binomial negative log likelihood.
Squared Error Regression $\sum_{i=1}^{N} (y_i - F(x_i))^2$Also called L2 loss. Default loss for regression tasks.
Absolute Error Regression $\sum_{i=1}^{N} |y_i - F(x_i)|$Also called L1 loss. Can be more robust to outliers than Squared Error.

Usage tips

We include a few guidelines for using GBTs by discussing the various parameters. We omit some decision tree parameters since those are covered in the decision tree guide.

Examples

GBTs currently have APIs in Scala and Java. Examples in both languages are shown below.

Classification

The example below demonstrates how to load a LIBSVM data file, parse it as an RDD of LabeledPoint and then perform classification using Gradient-Boosted Trees with log loss. The test error is calculated to measure the algorithm accuracy.

import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.util.MLUtils

// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a GradientBoostedTrees model.
//  The defaultParams for Classification use LogLoss by default.
val boostingStrategy = BoostingStrategy.defaultParams("Classification")
boostingStrategy.numIterations = 3 // Note: Use more iterations in practice.
boostingStrategy.treeStrategy.numClassesForClassification = 2
boostingStrategy.treeStrategy.maxDepth = 5
//  Empty categoricalFeaturesInfo indicates all features are continuous.
boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()

val model = GradientBoostedTrees.train(trainingData, boostingStrategy)

// Evaluate model on test instances and compute test error
val labelAndPreds = testData.map { point =>
  val prediction = model.predict(point.features)
  (point.label, prediction)
}
val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
println("Test Error = " + testErr)
println("Learned classification GBT model:\n" + model.toDebugString)
import scala.Tuple2;
import java.util.HashMap;
import java.util.Map;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.GradientBoostedTrees;
import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
import org.apache.spark.mllib.util.MLUtils;

SparkConf sparkConf = new SparkConf().setAppName("JavaGradientBoostedTrees");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

// Load and parse the data file.
String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();
// Split the data into training and test sets (30% held out for testing)
JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
JavaRDD<LabeledPoint> trainingData = splits[0];
JavaRDD<LabeledPoint> testData = splits[1];

// Train a GradientBoostedTrees model.
//  The defaultParams for Classification use LogLoss by default.
BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Classification");
boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
boostingStrategy.getTreeStrategy().setNumClassesForClassification(2);
boostingStrategy.getTreeStrategy().setMaxDepth(5);
//  Empty categoricalFeaturesInfo indicates all features are continuous.
Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);

final GradientBoostedTreesModel model =
  GradientBoostedTrees.train(trainingData, boostingStrategy);

// Evaluate model on test instances and compute test error
JavaPairRDD<Double, Double> predictionAndLabel =
  testData.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 testErr =
  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() / testData.count();
System.out.println("Test Error: " + testErr);
System.out.println("Learned classification GBT model:\n" + model.toDebugString());

Regression

The example below demonstrates how to load a LIBSVM data file, parse it as an RDD of LabeledPoint and then perform regression using Gradient-Boosted Trees with Squared Error as the loss. The Mean Squared Error (MSE) is computed at the end to evaluate goodness of fit.

import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.util.MLUtils

// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a GradientBoostedTrees model.
//  The defaultParams for Regression use SquaredError by default.
val boostingStrategy = BoostingStrategy.defaultParams("Regression")
boostingStrategy.numIterations = 3 // Note: Use more iterations in practice.
boostingStrategy.treeStrategy.maxDepth = 5
//  Empty categoricalFeaturesInfo indicates all features are continuous.
boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()

val model = GradientBoostedTrees.train(trainingData, boostingStrategy)

// Evaluate model on test instances and compute test error
val labelsAndPredictions = testData.map { point =>
  val prediction = model.predict(point.features)
  (point.label, prediction)
}
val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
println("Test Mean Squared Error = " + testMSE)
println("Learned regression GBT model:\n" + model.toDebugString)
import scala.Tuple2;
import java.util.HashMap;
import java.util.Map;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.GradientBoostedTrees;
import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
import org.apache.spark.mllib.util.MLUtils;

SparkConf sparkConf = new SparkConf().setAppName("JavaGradientBoostedTrees");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

// Load and parse the data file.
String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();
// Split the data into training and test sets (30% held out for testing)
JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
JavaRDD<LabeledPoint> trainingData = splits[0];
JavaRDD<LabeledPoint> testData = splits[1];

// Train a GradientBoostedTrees model.
//  The defaultParams for Regression use SquaredError by default.
BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Regression");
boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
boostingStrategy.getTreeStrategy().setMaxDepth(5);
//  Empty categoricalFeaturesInfo indicates all features are continuous.
Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);

final GradientBoostedTreesModel model =
  GradientBoostedTrees.train(trainingData, boostingStrategy);

// Evaluate model on test instances and compute test error
JavaPairRDD<Double, Double> predictionAndLabel =
  testData.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 testMSE =
  predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
    @Override
    public Double call(Tuple2<Double, Double> pl) {
      Double diff = pl._1() - pl._2();
      return diff * diff;
    }
  }).reduce(new Function2<Double, Double, Double>() {
    @Override
    public Double call(Double a, Double b) {
      return a + b;
    }
  }) / data.count();
System.out.println("Test Mean Squared Error: " + testMSE);
System.out.println("Learned regression GBT model:\n" + model.toDebugString());