Github user avulanov commented on the pull request:
https://github.com/apache/spark/pull/1155#issuecomment-70714725
@tolgap As documentation suggests, `MulticlassMetrics` accepts
`predictionAndLabels`, an RDD of (prediction, label) pairs, where `prediction`
is the predicted class/label, `label` is the actual class/label.
For example:
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.classification.ANNClassifier
import org.apache.spark.mllib.evaluation.MulticlassMetrics
/* load mnist data */
val data = MLUtils.loadLibSVMFile(sc, "mnist_file_in_svm_format")
val split = data.randomSplit(Array(0.9, 0.1), 11L)
val training = split(0)
val test = split(1)
/* train ANN with hidden layer of 32 neurons */
/* (input and output layer sizes will be derived from the data) */
val model = ANNClassifier.train(train, Array[Int](32), 40, 1.0, 1e-4)
val predictionAndLabels = test.map( lp => (model.predict(lp.features),
lp.label))
val metrics = new MulticlassMetrics(predictionAndLabels)
println("Accuracy:" + metrics.precision)
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