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
--------- 

.. 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.
      * - 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")