# Random Forest Regression Random forests are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on random forests. 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 tree-based algorithms 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.{RandomForestRegressionModel, RandomForestRegressor} // Load and parse the data file, converting it to 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. // Set maxCategories so features with > 4 distinct values are treated 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 RandomForest model. val rf = new RandomForestRegressor() .setLabelCol("label") .setFeaturesCol("indexedFeatures") // Chain indexer and forest in a Pipeline. val pipeline = new Pipeline() .setStages(Array(featureIndexer, rf)) // 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 rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel] println(s"Learned regression forest model:\n ${rfModel.toDebugString}") /* Output: +----------+-----+--------------------+ |prediction|label| features| +----------+-----+--------------------+ | 0.0| 0.0|(692,[98,99,100,1...| | 0.1| 0.0|(692,[122,123,148...| | 0.0| 0.0|(692,[123,124,125...| | 0.0| 0.0|(692,[124,125,126...| | 0.0| 0.0|(692,[124,125,126...| +----------+-----+--------------------+ only showing top 5 rows Root Mean Squared Error (RMSE) on test data = 0.14711511527663346 Learned regression forest model: RandomForestRegressionModel (uid=rfr_f01d85b28d5a) with 20 trees Tree 0 (weight 1.0): If (feature 434 <= 88.5) Predict: 0.0 Else (feature 434 > 88.5) Predict: 1.0 Tree 1 (weight 1.0): If (feature 490 <= 15.5) Predict: 0.0 Else (feature 490 > 15.5) Predict: 1.0 Tree 2 (weight 1.0): If (feature 462 <= 62.5) Predict: 0.0 Else (feature 462 > 62.5) Predict: 1.0 Tree 3 (weight 1.0): If (feature 461 <= 71.0) If (feature 343 <= 253.5) Predict: 0.0 Else (feature 343 > 253.5) Predict: 1.0 Else (feature 461 > 71.0) Predict: 1.0 Tree 4 (weight 1.0): If (feature 483 <= 15.5) If (feature 318 <= 223.0) Predict: 1.0 Else (feature 318 > 223.0) Predict: 0.0 Else (feature 483 > 15.5) Predict: 0.0 Tree 5 (weight 1.0): If (feature 405 <= 106.0) If (feature 490 <= 15.5) Predict: 0.0 Else (feature 490 > 15.5) Predict: 1.0 Else (feature 405 > 106.0) Predict: 1.0 Tree 6 (weight 1.0): If (feature 490 <= 44.5) Predict: 0.0 Else (feature 490 > 44.5) Predict: 1.0 Tree 7 (weight 1.0): If (feature 400 <= 4.5) If (feature 375 <= 103.0) Predict: 1.0 Else (feature 375 > 103.0) Predict: 0.0 Else (feature 400 > 4.5) Predict: 0.0 Tree 8 (weight 1.0): If (feature 406 <= 126.5) Predict: 0.0 Else (feature 406 > 126.5) Predict: 1.0 Tree 9 (weight 1.0): If (feature 490 <= 44.5) Predict: 0.0 Else (feature 490 > 44.5) Predict: 1.0 Tree 10 (weight 1.0): If (feature 345 <= 6.5) Predict: 1.0 Else (feature 345 > 6.5) Predict: 0.0 Tree 11 (weight 1.0): If (feature 406 <= 126.5) If (feature 436 <= 1.5) Predict: 0.0 Else (feature 436 > 1.5) Predict: 1.0 Else (feature 406 > 126.5) Predict: 1.0 Tree 12 (weight 1.0): If (feature 489 <= 1.5) Predict: 0.0 Else (feature 489 > 1.5) Predict: 1.0 Tree 13 (weight 1.0): If (feature 462 <= 62.5) Predict: 0.0 Else (feature 462 > 62.5) Predict: 1.0 Tree 14 (weight 1.0): If (feature 435 <= 32.5) If (feature 488 <= 141.0) Predict: 0.0 Else (feature 488 > 141.0) Predict: 1.0 Else (feature 435 > 32.5) Predict: 1.0 Tree 15 (weight 1.0): If (feature 489 <= 1.5) If (feature 519 <= 146.0) Predict: 0.0 Else (feature 519 > 146.0) Predict: 1.0 Else (feature 489 > 1.5) Predict: 1.0 Tree 16 (weight 1.0): If (feature 434 <= 88.5) Predict: 0.0 Else (feature 434 > 88.5) Predict: 1.0 Tree 17 (weight 1.0): If (feature 378 <= 18.0) Predict: 0.0 Else (feature 378 > 18.0) Predict: 1.0 Tree 18 (weight 1.0): If (feature 434 <= 88.5) Predict: 0.0 Else (feature 434 > 88.5) Predict: 1.0 Tree 19 (weight 1.0): If (feature 490 <= 44.5) Predict: 0.0 Else (feature 490 > 44.5) Predict: 1.0 */ ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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