Binary Classification Evaluator

Evaluator for binary classification, which expects two input columns: rawPrediction and label.

Output

It outputs the Probability for each class

Type

ml-evaluator

Class

fire.nodes.ml.NodeBinaryClassificationEvaluator

Fields

Name	Title	Description
labelCol	Label Column	The label column for model fitting.
predictionCol	Prediction Column	The prediction column.
modelUUID	Model UUID	Enter the model uuid
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
path	Save Confusion Matrix Path	Save Confusion Matrix
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

Evaluator for binary classification, which expects two input columns: rawPrediction and label.

More at Spark MLlib/ML docs page : http://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#binary-classification

Examples

Below example is available at : https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#binary-classification

import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS

import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics

import org.apache.spark.mllib.regression.LabeledPoint

import org.apache.spark.mllib.util.MLUtils

// Load training data in LIBSVM format

val data = MLUtils.loadLibSVMFile(sc, “data/mllib/sample_binary_classification_data.txt”)

// Split data into training (60%) and test (40%)

val Array(training, test) = data.randomSplit(Array(0.6, 0.4), seed = 11L)

training.cache()

// Run training algorithm to build the model

val model = new LogisticRegressionWithLBFGS()

.setNumClasses(2)

.run(training)

// Clear the prediction threshold so the model will return probabilities

model.clearThreshold

// Compute raw scores on the test set

val predictionAndLabels = test.map { case LabeledPoint(label, features) =>

val prediction = model.predict(features)

(prediction, label)

}

// Instantiate metrics object

val metrics = new BinaryClassificationMetrics(predictionAndLabels)

// Precision by threshold

val precision = metrics.precisionByThreshold

precision.collect.foreach { case (t, p) =>

println(s”Threshold: $t, Precision: $p”)

}

// Recall by threshold

val recall = metrics.recallByThreshold

recall.collect.foreach { case (t, r) =>

println(s”Threshold: $t, Recall: $r”)

}

// Precision-Recall Curve

val PRC = metrics.pr

// F-measure

val f1Score = metrics.fMeasureByThreshold

f1Score.collect.foreach { case (t, f) =>

println(s”Threshold: $t, F-score: $f, Beta = 1”)

}

val beta = 0.5

val fScore = metrics.fMeasureByThreshold(beta)

fScore.collect.foreach { case (t, f) =>

println(s”Threshold: $t, F-score: $f, Beta = 0.5”)

}

// AUPRC

val auPRC = metrics.areaUnderPR

println(s”Area under precision-recall curve = $auPRC”)

// Compute thresholds used in ROC and PR curves

val thresholds = precision.map(_._1)

// ROC Curve

val roc = metrics.roc

// AUROC

val auROC = metrics.areaUnderROC

println(s”Area under ROC = $auROC”)