Interface BinaryClassificationSummary
 All Superinterfaces:
ClassificationSummary
,Serializable
,scala.Serializable
 All Known Subinterfaces:
BinaryLogisticRegressionSummary
,BinaryLogisticRegressionTrainingSummary
,BinaryRandomForestClassificationSummary
,BinaryRandomForestClassificationTrainingSummary
,FMClassificationSummary
,FMClassificationTrainingSummary
,LinearSVCSummary
,LinearSVCTrainingSummary
 All Known Implementing Classes:
BinaryLogisticRegressionSummaryImpl
,BinaryLogisticRegressionTrainingSummaryImpl
,BinaryRandomForestClassificationSummaryImpl
,BinaryRandomForestClassificationTrainingSummaryImpl
,FMClassificationSummaryImpl
,FMClassificationTrainingSummaryImpl
,LinearSVCSummaryImpl
,LinearSVCTrainingSummaryImpl
Abstraction for binary classification results for a given model.

Method Summary
Modifier and TypeMethodDescriptiondouble
Computes the area under the receiver operating characteristic (ROC) curve.Returns a dataframe with two fields (threshold, FMeasure) curve with beta = 1.0.pr()
Returns the precisionrecall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.Returns a dataframe with two fields (threshold, precision) curve.Returns a dataframe with two fields (threshold, recall) curve.roc()
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.scoreCol()
Field in "predictions" which gives the probability or rawPrediction of each class as a vector.Methods inherited from interface org.apache.spark.ml.classification.ClassificationSummary
accuracy, falsePositiveRateByLabel, fMeasureByLabel, fMeasureByLabel, labelCol, labels, precisionByLabel, predictionCol, predictions, recallByLabel, truePositiveRateByLabel, weightCol, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate

Method Details

areaUnderROC
double areaUnderROC()Computes the area under the receiver operating characteristic (ROC) curve. Returns:
 (undocumented)

fMeasureByThreshold
Returns a dataframe with two fields (threshold, FMeasure) curve with beta = 1.0. Returns:
 (undocumented)

pr
Returns the precisionrecall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it. Returns:
 (undocumented)

precisionByThreshold
Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision. Returns:
 (undocumented)

recallByThreshold
Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall. Returns:
 (undocumented)

roc
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic Returns:
 (undocumented)

scoreCol
String scoreCol()Field in "predictions" which gives the probability or rawPrediction of each class as a vector. Returns:
 (undocumented)
