Decision Tree Regression

Decision trees are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on decision trees.
Examples
The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. We use a feature transformer to index categorical features, adding metadata to the DataFrame which the Decision Tree algorithm can recognize.
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.VectorIndexer
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.regression.DecisionTreeRegressor
​
// Load the data stored in LIBSVM format as a DataFrame.
val data = spark.read.format("libsvm").load("file:///opt/spark/data/mllib/sample_libsvm_data.txt")
​
// Automatically identify categorical features, and index them.
// Here, we treat features with > 4 distinct values as continuous.
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(4)
.fit(data)
​
// Split the data into training and test sets (30% held out for testing).
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
​
// Train a DecisionTree model.
val dt = new DecisionTreeRegressor()
.setLabelCol("label")
.setFeaturesCol("indexedFeatures")
​
// Chain indexer and tree in a Pipeline.
val pipeline = new Pipeline()
.setStages(Array(featureIndexer, dt))
​
// Train model. This also runs the indexer.
val model = pipeline.fit(trainingData)
​
// Make predictions.
val predictions = model.transform(testData)
​
// Select example rows to display.
predictions.select("prediction", "label", "features").show(5)
​
// Select (prediction, true label) and compute test error.
val evaluator = new RegressionEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("rmse")
val rmse = evaluator.evaluate(predictions)
println(s"Root Mean Squared Error (RMSE) on test data = $rmse")
​
val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel]
println(s"Learned regression tree model:\n ${treeModel.toDebugString}")
​
/*
Output:
+----------+-----+--------------------+
|prediction|label| features|
+----------+-----+--------------------+
| 0.0| 0.0|(692,[123,124,125...|
| 0.0| 0.0|(692,[124,125,126...|
| 0.0| 0.0|(692,[124,125,126...|
| 0.0| 0.0|(692,[126,127,128...|
| 0.0| 0.0|(692,[126,127,128...|
+----------+-----+--------------------+
only showing top 5 rows
​
Root Mean Squared Error (RMSE) on test data = 0.19611613513818404
Learned regression tree model:
DecisionTreeRegressionModel (uid=dtr_f30a452bc6d9) of depth 2 with 5 nodes
If (feature 406 <= 126.5)
If (feature 99 in {0.0,3.0})
Predict: 0.0
Else (feature 99 not in {0.0,3.0})
Predict: 1.0
Else (feature 406 > 126.5)
Predict: 1.0
​
​
*/