RandomForestRegressor

RandomForestRegressor is a Predictor for Random Forest machine learning algorithm that trains a RandomForestRegressionModel.

import org.apache.spark.mllib.linalg.Vectors
val features = Vectors.sparse(10, Seq((2, 0.2), (4, 0.4)))

val data = (0.0 to 4.0 by 1).map(d => (d, features)).toDF("label", "features")
// data.as[LabeledPoint]

scala> data.show(false)
+-----+--------------------------+
|label|features                  |
+-----+--------------------------+
|0.0  |(10,[2,4,6],[0.2,0.4,0.6])|
|1.0  |(10,[2,4,6],[0.2,0.4,0.6])|
|2.0  |(10,[2,4,6],[0.2,0.4,0.6])|
|3.0  |(10,[2,4,6],[0.2,0.4,0.6])|
|4.0  |(10,[2,4,6],[0.2,0.4,0.6])|
+-----+--------------------------+

import org.apache.spark.ml.regression.{ RandomForestRegressor, RandomForestRegressionModel }
val rfr = new RandomForestRegressor
val model: RandomForestRegressionModel = rfr.fit(data)

scala> model.trees.foreach(println)
DecisionTreeRegressionModel (uid=dtr_247e77e2f8e0) of depth 1 with 3 nodes
DecisionTreeRegressionModel (uid=dtr_61f8eacb2b61) of depth 2 with 7 nodes
DecisionTreeRegressionModel (uid=dtr_63fc5bde051c) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_64d4e42de85f) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_693626422894) of depth 3 with 9 nodes
DecisionTreeRegressionModel (uid=dtr_927f8a0bc35e) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_82da39f6e4e1) of depth 3 with 7 nodes
DecisionTreeRegressionModel (uid=dtr_cb94c2e75bd1) of depth 0 with 1 nodes
DecisionTreeRegressionModel (uid=dtr_29e3362adfb2) of depth 1 with 3 nodes
DecisionTreeRegressionModel (uid=dtr_d6d896abcc75) of depth 3 with 7 nodes
DecisionTreeRegressionModel (uid=dtr_aacb22a9143d) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_18d07dadb5b9) of depth 2 with 7 nodes
DecisionTreeRegressionModel (uid=dtr_f0615c28637c) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_4619362d02fc) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_d39502f828f4) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_896f3a4272ad) of depth 3 with 9 nodes
DecisionTreeRegressionModel (uid=dtr_891323c29838) of depth 3 with 7 nodes
DecisionTreeRegressionModel (uid=dtr_d658fe871e99) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_d91227b13d41) of depth 2 with 5 nodes
DecisionTreeRegressionModel (uid=dtr_4a7976921f4b) of depth 2 with 5 nodes

scala> model.treeWeights
res12: Array[Double] = Array(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)

scala> model.featureImportances
res13: org.apache.spark.mllib.linalg.Vector = (1,[0],[1.0])

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