Random Forest Classifier

Supports both binary and multiclass labels, as well as both continuous and categorical features.

Input

Takes in a DataFrame and performs Random Forest Classification

Output

Random Forest Classification 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.NodeRandomForestClassifier

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
path	Path	Save Confusion Matrix to Path
featureSubsetStrategy	Feature Subset Strategy	The number of features to consider for splits at each tree node.
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
numTrees	Num Trees	The number of trees to train
subsamplingRate	Subsampling Rate	The fraction of the training data used for learning each decision tree.
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
bootstrap	Bootstrap	Whether bootstrap samples are used when building trees.
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
numTreesGrid	Number trees Param Grid Search	Total number of trees Parameters for Grid Search
confusionMatrix	Confusion Matrix
output_confusion_matrix_chart	Output Confusion Matrix Chart	Whether to display Confusion Matrix Chart.
cmChartTitle	Confusion Matrix Chart Title	Title name to display in Confusion Matrix Chart
cmChartDescription	Confusion Matrix Chart Description	Description to display in Confusion Matrix Chart
confusionMatrixTargetLegend	Confusion Matrix Target Legend	Legend name to display for Target in Confusion Matrix
confusionMatrixPredictedLabelLegend	Confusion Matrix PredictedLabel Legend	Legend name to display for Predicted Label in Confusion Matrix
Description	Confusion Matrix Description
confusionMatrixRowDescription	Confusion Matrix Outcome description	Add the business details of the outcome of the confusion matrix rows
ROC Curve	ROC Curve
output_roc_chart	Output ROC Curve	Whether to display confusion matrix chart.
roc_title	ROC Curve Chart Title	Title name to display in ROC Curve Chart
roc_description	ROC Curve Chart Description	Add Description for ROC Curve Chart
xlabel	X Label	X label
ylabel	Y Label	Y Label

Details

Random forests are a popular family of classification and regression methods.

Random forests supports both binary and multiclass labels, as well as both continuous and categorical features.

Random forests are ensembles of decision trees. Random forests combine many decision trees in order to reduce the risk of overfitting. The spark.ml implementation supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features.

More details are available at Apache Spark ML docs page:

http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier

Examples

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

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.labelsArray(0))

// 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}”)