Multiclass classification error metrics in Apache Spark

From MLlib docs

Assuming your test data is in test

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

val predictionAndLabels = test.map { case LabeledPoint(label,
   features) =>
     val prediction = model.predict(features)
     (prediction, label)
}

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}")