Multiclass Classification Evaluator
===========

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

Type
--------- 

ml-evaluator

Class
--------- 

fire.nodes.ml.NodeMulticlassClassificationEvaluator

Fields
--------- 

.. list-table::
      :widths: 10 5 10
      :header-rows: 1

      * - 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}")