Decision Tree Regression

It supports both continuous and categorical features.

Input

This takes in a DataFrame and performs Decision Tree Regression

Output

The Decision Tree Regression Model generated is passed along to the next nodes. The input DataFrame is also passed along to the next nodes

Type

ml-estimator

Class

fire.nodes.ml.NodeDecisionTreeRegression

Fields

Name	Title	Description
featuresCol	Features Column	Features column of type vectorUDT for model fitting
labelCol	Label Column	The label column for model fitting
predictionCol	Prediction Column	The prediction column created during model scoring.
splitRatio	Split Ratio	Split Ratio
impurity	Impurity	The Criterion used for information gain calculation
maxBins	Max Bins	The maximum number of bins used for discretizing continuous features.Must be >= 2 and >= number of categories in any categorical feature.
maxDepth	Max Depth	The Maximum depth of a tree
minInfoGain	Min Information Gain	The Minimum information gain for a split to be considered at a tree node
minInstancesPerNode	Min Instances Per Node	The Minimum number of instances each child must have after split
seed	Seed	The random seed
cacheNodeIds	Cache Node Ids	The caching nodes IDs. Can speed up training of deeper trees.
checkpointInterval	Checkpoint Interval	The checkpoint interval. E.g. 10 means that the cache will get checkpointed every 10 iterations.Set checkpoint interval (>= 1) or disable checkpoint (-1)
maxMemoryInMB	Max memory	Maximum memory in MB allocated to histogram aggregation.
minWeightFractionPerNode	Min Weight Fraction per Node	Minimum fraction of the weighted sample count that each child must have after split.
weightCol	Weight Column	Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
gridSearch	Grid Search
minInfoGainGrid	Min Information Gain Param Grid Search	Min Information Gain Parameters for Grid Search
maxBinsGrid	Max Bins Param Grid Search	Max Bins Parameters for Grid Search
maxDepthGrid	Max Depth Param Grid Search	Max Depth Parameters for Grid Search

Details

Decision tree supports both continuous and categorical features.

More details are available at Apache Spark ML docs page : https://spark.apache.org/docs/1.6.0/ml-classification-regression.html#decision-tree-regression

Examples

Below example is available at : https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-regression

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(“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}”)