MLlib  Decision Tree
Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture nonlinearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks.
MLlib supports decision trees for binary and multiclass classification and for regression, using both continuous and categorical features. The implementation partitions data by rows, allowing distributed training with millions of instances.
Ensembles of trees (Random Forests and GradientBoosted Trees) are described in the Ensembles guide.
Basic algorithm
The decision tree is a greedy algorithm that performs a recursive binary partitioning of the feature
space. The tree predicts the same label for each bottommost (leaf) partition.
Each partition is chosen greedily by selecting the best split from a set of possible splits,
in order to maximize the information gain at a tree node. In other words, the split chosen at each
tree node is chosen from the set $\underset{s}{\operatorname{argmax}} IG(D,s)$
where $IG(D,s)$
is the information gain when a split $s$
is applied to a dataset $D$
.
Node impurity and information gain
The node impurity is a measure of the homogeneity of the labels at the node. The current implementation provides two impurity measures for classification (Gini impurity and entropy) and one impurity measure for regression (variance).
Impurity  Task  Formula  Description 

Gini impurity  Classification  $\sum_{i=1}^{C} f_i(1f_i)$  $f_i$ is the frequency of label $i$ at a node and $C$ is the number of unique labels. 
Entropy  Classification  $\sum_{i=1}^{C} f_ilog(f_i)$  $f_i$ is the frequency of label $i$ at a node and $C$ is the number of unique labels. 
Variance  Regression  $\frac{1}{N} \sum_{i=1}^{N} (x_i  \mu)^2$  $y_i$ is label for an instance, $N$ is the number of instances and $\mu$ is the mean given by $\frac{1}{N} \sum_{i=1}^N x_i$. 
The information gain is the difference between the parent node impurity and the weighted sum of
the two child node impurities. Assuming that a split $s$ partitions the dataset $D$
of size $N$
into two datasets $D_{left}$
and $D_{right}$
of sizes $N_{left}$
and $N_{right}$
,
respectively, the information gain is:
$IG(D,s) = Impurity(D)  \frac{N_{left}}{N} Impurity(D_{left})  \frac{N_{right}}{N} Impurity(D_{right})$
Split candidates
Continuous features
For small datasets in singlemachine implementations, the split candidates for each continuous feature are typically the unique values for the feature. Some implementations sort the feature values and then use the ordered unique values as split candidates for faster tree calculations.
Sorting feature values is expensive for large distributed datasets.
This implementation computes an approximate set of split candidates by performing a quantile
calculation over a sampled fraction of the data.
The ordered splits create “bins” and the maximum number of such
bins can be specified using the maxBins
parameter.
Note that the number of bins cannot be greater than the number of instances $N$
(a rare scenario
since the default maxBins
value is 32). The tree algorithm automatically reduces the number of
bins if the condition is not satisfied.
Categorical features
For a categorical feature with $M$
possible values (categories), one could come up with
$2^{M1}1$
split candidates. For binary (0/1) classification and regression,
we can reduce the number of split candidates to $M1$
by ordering the
categorical feature values by the average label. (See Section 9.2.4 in
Elements of Statistical Machine Learning for
details.) For example, for a binary classification problem with one categorical feature with three
categories A, B and C whose corresponding proportions of label 1 are 0.2, 0.6 and 0.4, the categorical
features are ordered as A, C, B. The two split candidates are A  C, B
and A , C  B where  denotes the split.
In multiclass classification, all $2^{M1}1$
possible splits are used whenever possible.
When $2^{M1}1$
is greater than the maxBins
parameter, we use a (heuristic) method
similar to the method used for binary classification and regression.
The $M$
categorical feature values are ordered by impurity,
and the resulting $M1$
split candidates are considered.
Stopping rule
The recursive tree construction is stopped at a node when one of the following conditions is met:
 The node depth is equal to the
maxDepth
training parameter.  No split candidate leads to an information gain greater than
minInfoGain
.  No split candidate produces child nodes which each have at least
minInstancesPerNode
training instances.
Usage tips
We include a few guidelines for using decision trees by discussing the various parameters.
The parameters are listed below roughly in order of descending importance. New users should mainly consider the “Problem specification parameters” section and the maxDepth
parameter.
Problem specification parameters
These parameters describe the problem you want to solve and your dataset. They should be specified and do not require tuning.

algo
:Classification
orRegression

numClasses
: Number of classes (forClassification
only) 
categoricalFeaturesInfo
: Specifies which features are categorical and how many categorical values each of those features can take. This is given as a map from feature indices to feature arity (number of categories). Any features not in this map are treated as continuous. E.g.,
Map(0 > 2, 4 > 10)
specifies that feature0
is binary (taking values0
or1
) and that feature4
has 10 categories (values{0, 1, ..., 9}
). Note that feature indices are 0based: features0
and4
are the 1st and 5th elements of an instance’s feature vector.  Note that you do not have to specify
categoricalFeaturesInfo
. The algorithm will still run and may get reasonable results. However, performance should be better if categorical features are properly designated.
 E.g.,
Stopping criteria
These parameters determine when the tree stops building (adding new nodes). When tuning these parameters, be careful to validate on heldout test data to avoid overfitting.

maxDepth
: Maximum depth of a tree. Deeper trees are more expressive (potentially allowing higher accuracy), but they are also more costly to train and are more likely to overfit. 
minInstancesPerNode
: For a node to be split further, each of its children must receive at least this number of training instances. This is commonly used with RandomForest since those are often trained deeper than individual trees. 
minInfoGain
: For a node to be split further, the split must improve at least this much (in terms of information gain).
Tunable parameters
These parameters may be tuned. Be careful to validate on heldout test data when tuning in order to avoid overfitting.
maxBins
: Number of bins used when discretizing continuous features. Increasing
maxBins
allows the algorithm to consider more split candidates and make finegrained split decisions. However, it also increases computation and communication.  Note that the
maxBins
parameter must be at least the maximum number of categories$M$
for any categorical feature.
 Increasing
maxMemoryInMB
: Amount of memory to be used for collecting sufficient statistics. The default value is conservatively chosen to be 256 MB to allow the decision algorithm to work in most scenarios. Increasing
maxMemoryInMB
can lead to faster training (if the memory is available) by allowing fewer passes over the data. However, there may be decreasing returns asmaxMemoryInMB
grows since the amount of communication on each iteration can be proportional tomaxMemoryInMB
.  Implementation details: For faster processing, the decision tree algorithm collects statistics about groups of nodes to split (rather than 1 node at a time). The number of nodes which can be handled in one group is determined by the memory requirements (which vary per features). The
maxMemoryInMB
parameter specifies the memory limit in terms of megabytes which each worker can use for these statistics.
 The default value is conservatively chosen to be 256 MB to allow the decision algorithm to work in most scenarios. Increasing

subsamplingRate
: Fraction of the training data used for learning the decision tree. This parameter is most relevant for training ensembles of trees (usingRandomForest
andGradientBoostedTrees
), where it can be useful to subsample the original data. For training a single decision tree, this parameter is less useful since the number of training instances is generally not the main constraint. impurity
: Impurity measure (discussed above) used to choose between candidate splits. This measure must match thealgo
parameter.
Caching and checkpointing
MLlib 1.2 adds several features for scaling up to larger (deeper) trees and tree ensembles. When maxDepth
is set to be large, it can be useful to turn on node ID caching and checkpointing. These parameters are also useful for RandomForest when numTrees
is set to be large.
useNodeIdCache
: If this is set to true, the algorithm will avoid passing the current model (tree or trees) to executors on each iteration. This can be useful with deep trees (speeding up computation on workers) and for large Random Forests (reducing communication on each iteration).
 Implementation details: By default, the algorithm communicates the current model to executors so that executors can match training instances with tree nodes. When this setting is turned on, then the algorithm will instead cache this information.
Node ID caching generates a sequence of RDDs (1 per iteration). This long lineage can cause performance problems, but checkpointing intermediate RDDs can alleviate those problems.
Note that checkpointing is only applicable when useNodeIdCache
is set to true.

checkpointDir
: Directory for checkpointing node ID cache RDDs. 
checkpointInterval
: Frequency for checkpointing node ID cache RDDs. Setting this too low will cause extra overhead from writing to HDFS; setting this too high can cause problems if executors fail and the RDD needs to be recomputed.
Scaling
Computation scales approximately linearly in the number of training instances,
in the number of features, and in the maxBins
parameter.
Communication scales approximately linearly in the number of features and in maxBins
.
The implemented algorithm reads both sparse and dense data. However, it is not optimized for sparse input.
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 decision tree with Gini impurity as an impurity measure and a
maximum tree depth of 5. The test error is calculated to measure the algorithm accuracy.
import org.apache.spark.mllib.tree.DecisionTree
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 DecisionTree model.
// Empty categoricalFeaturesInfo indicates all features are continuous.
val numClasses = 2
val categoricalFeaturesInfo = Map[Int, Int]()
val impurity = "gini"
val maxDepth = 5
val maxBins = 32
val model = DecisionTree.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
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 tree model:\n" + model.toDebugString)
import java.util.HashMap;
import scala.Tuple2;
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.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;
SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
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.
Integer numClasses = 2;
Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
String impurity = "gini";
Integer maxDepth = 5;
Integer maxBins = 32;
// Train a DecisionTree model for classification.
final DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses,
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 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 tree model:\n" + model.toDebugString());
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
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 DecisionTree model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
impurity='gini', maxDepth=5, 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 tree 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 decision tree with variance as an impurity measure and a maximum tree
depth of 5. The Mean Squared Error (MSE) is computed at the end to evaluate
goodness of fit.
import org.apache.spark.mllib.tree.DecisionTree
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 DecisionTree model.
// Empty categoricalFeaturesInfo indicates all features are continuous.
val categoricalFeaturesInfo = Map[Int, Int]()
val impurity = "variance"
val maxDepth = 5
val maxBins = 32
val model = DecisionTree.trainRegressor(trainingData, categoricalFeaturesInfo, 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 tree 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.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;
SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
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 = 5;
Integer maxBins = 32;
// Train a DecisionTree model.
final DecisionTreeModel model = DecisionTree.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;
}
}) / data.count();
System.out.println("Test Mean Squared Error: " + testMSE);
System.out.println("Learned regression tree model:\n" + model.toDebugString());
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
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 DecisionTree model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
model = DecisionTree.trainRegressor(trainingData, categoricalFeaturesInfo={},
impurity='variance', maxDepth=5, 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 tree model:')
print(model.toDebugString())