# Random forests 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 two feature transformers to prepare the data; these help index categories for the label and 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.classification.{RandomForestClassificationModel, RandomForestClassifier} import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer} // Load and parse the data file, converting it to a DataFrame. val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") // Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. val labelIndexer = new StringIndexer() .setInputCol("label") .setOutputCol("indexedLabel") .fit(data) // 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 RandomForestClassifier() .setLabelCol("indexedLabel") .setFeaturesCol("indexedFeatures") .setNumTrees(10) // Convert indexed labels back to original labels. val labelConverter = new IndexToString() .setInputCol("prediction") .setOutputCol("predictedLabel") .setLabels(labelIndexer.labels) // Chain indexers and forest in a Pipeline. val pipeline = new Pipeline() .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter)) // Train model. This also runs the indexers. val model = pipeline.fit(trainingData) // Make predictions. val predictions = model.transform(testData) // Select example rows to display. predictions.select("predictedLabel", "label", "features").show(5) // Select (prediction, true label) and compute test error. val evaluator = new MulticlassClassificationEvaluator() .setLabelCol("indexedLabel") .setPredictionCol("prediction") .setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) println(s"Test Error = ${(1.0 - accuracy)}") val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] println(s"Learned classification forest model:\n ${rfModel.toDebugString}") /* Output: +--------------+-----+--------------------+ |predictedLabel|label| features| +--------------+-----+--------------------+ | 0.0| 0.0|(692,[95,96,97,12...| | 0.0| 0.0|(692,[98,99,100,1...| | 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 Test Error = 0.0 Learned classification forest model: RandomForestClassificationModel (uid=rfc_b10be44ab730) with 10 trees Tree 0 (weight 1.0): If (feature 510 <= 2.5) If (feature 495 <= 111.0) Predict: 0.0 Else (feature 495 > 111.0) Predict: 1.0 Else (feature 510 > 2.5) Predict: 1.0 Tree 1 (weight 1.0): If (feature 567 <= 8.0) If (feature 456 <= 31.5) If (feature 373 <= 11.5) Predict: 0.0 Else (feature 373 > 11.5) Predict: 1.0 Else (feature 456 > 31.5) Predict: 1.0 Else (feature 567 > 8.0) If (feature 317 <= 9.5) Predict: 0.0 Else (feature 317 > 9.5) If (feature 491 <= 49.5) Predict: 1.0 Else (feature 491 > 49.5) Predict: 0.0 Tree 2 (weight 1.0): If (feature 540 <= 87.0) If (feature 576 <= 221.5) Predict: 0.0 Else (feature 576 > 221.5) If (feature 490 <= 15.5) Predict: 1.0 Else (feature 490 > 15.5) Predict: 0.0 Else (feature 540 > 87.0) Predict: 1.0 Tree 3 (weight 1.0): If (feature 518 <= 18.0) If (feature 350 <= 97.5) Predict: 1.0 Else (feature 350 > 97.5) If (feature 356 <= 16.0) Predict: 0.0 Else (feature 356 > 16.0) Predict: 1.0 Else (feature 518 > 18.0) Predict: 0.0 Tree 4 (weight 1.0): If (feature 429 <= 11.5) If (feature 358 <= 12.0) Predict: 0.0 Else (feature 358 > 12.0) Predict: 1.0 Else (feature 429 > 11.5) Predict: 1.0 Tree 5 (weight 1.0): If (feature 462 <= 62.5) If (feature 240 <= 253.5) Predict: 1.0 Else (feature 240 > 253.5) Predict: 0.0 Else (feature 462 > 62.5) Predict: 0.0 Tree 6 (weight 1.0): If (feature 385 <= 4.0) If (feature 545 <= 3.0) If (feature 346 <= 2.0) Predict: 0.0 Else (feature 346 > 2.0) Predict: 1.0 Else (feature 545 > 3.0) Predict: 0.0 Else (feature 385 > 4.0) Predict: 1.0 Tree 7 (weight 1.0): If (feature 512 <= 8.0) If (feature 350 <= 7.0) If (feature 298 <= 152.5) Predict: 1.0 Else (feature 298 > 152.5) Predict: 0.0 Else (feature 350 > 7.0) Predict: 0.0 Else (feature 512 > 8.0) Predict: 1.0 Tree 8 (weight 1.0): If (feature 462 <= 62.5) If (feature 324 <= 253.5) Predict: 1.0 Else (feature 324 > 253.5) Predict: 0.0 Else (feature 462 > 62.5) Predict: 0.0 Tree 9 (weight 1.0): If (feature 301 <= 30.0) If (feature 517 <= 20.5) If (feature 630 <= 5.0) Predict: 0.0 Else (feature 630 > 5.0) Predict: 1.0 Else (feature 517 > 20.5) Predict: 0.0 Else (feature 301 > 30.0) 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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