Multiclass Classification Evaluator

Evaluator for multiclass classification, which expects two input columns: score and label.

Type

ml-evaluator

Class

fire.nodes.ml.NodeMulticlassClassificationEvaluator

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.
cm_chart_title	Confusion Matrix Chart Title	Title name to display in Confusion Matrix Chart
cm_chart_description	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
confusionMatrixCountLegend	Confusion Matrix Count Legend	Legend name to display for Count in Confusion Matrix
Description	Confusion Matrix Description
confusionMatrixRowDescription	Confusion Matrix Outcome description	One can provide the business details of the outcome of the confusion matrix rows

Details

Evaluator for multiclass classification, which expects two input columns: score and label.

More at Spark MLlib/ML docs page :https://spark.apache.org/docs/1.6.0/mllib-evaluation-metrics.html#multiclass-classification

Examples

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

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

import org.apache.spark.mllib.evaluation.MulticlassMetrics

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_multiclass_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(3)

.run(training)

// 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 MulticlassMetrics(predictionAndLabels)

// Confusion matrix

println(“Confusion matrix:”)

println(metrics.confusionMatrix)

// Overall Statistics

val accuracy = metrics.accuracy

println(“Summary Statistics”)

println(s”Accuracy = $accuracy”)

// Precision by label

val labels = metrics.labels

labels.foreach { l =>

println(s”Precision($l) = ” + metrics.precision(l))

}

// Recall by label

labels.foreach { l =>

println(s”Recall($l) = ” + metrics.recall(l))

}

// False positive rate by label

labels.foreach { l =>

println(s”FPR($l) = ” + metrics.falsePositiveRate(l))

}

// F-measure by label

labels.foreach { l =>

println(s”F1-Score($l) = ” + metrics.fMeasure(l))

}

// Weighted stats

println(s”Weighted precision: ${metrics.weightedPrecision}”)

println(s”Weighted recall: ${metrics.weightedRecall}”)

println(s”Weighted F1 score: ${metrics.weightedFMeasure}”)

println(s”Weighted false positive rate: ${metrics.weightedFalsePositiveRate}”)