org.apache.spark.ml.classification

Interface BinaryClassificationSummary

All Superinterfaces:

ClassificationSummary, java.io.Serializable

All Known Subinterfaces:

BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary, BinaryRandomForestClassificationSummary, BinaryRandomForestClassificationTrainingSummary, FMClassificationSummary, FMClassificationTrainingSummary, LinearSVCSummary, LinearSVCTrainingSummary

All Known Implementing Classes:

BinaryLogisticRegressionSummaryImpl, BinaryLogisticRegressionTrainingSummaryImpl, BinaryRandomForestClassificationSummaryImpl, BinaryRandomForestClassificationTrainingSummaryImpl, FMClassificationSummaryImpl, FMClassificationTrainingSummaryImpl, LinearSVCSummaryImpl, LinearSVCTrainingSummaryImpl

public interface BinaryClassificationSummary
extends ClassificationSummary

Abstraction for binary classification results for a given model.

Method Summary

Modifier and Type	Method and Description
double	areaUnderROC() Computes the area under the receiver operating characteristic (ROC) curve.
Dataset<Row>	fMeasureByThreshold() Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
Dataset<Row>	pr() Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Dataset<Row>	precisionByThreshold() Returns a dataframe with two fields (threshold, precision) curve.
Dataset<Row>	recallByThreshold() Returns a dataframe with two fields (threshold, recall) curve.
Dataset<Row>	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.
String	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 Detail

areaUnderROC

double areaUnderROC()

Computes the area under the receiver operating characteristic (ROC) curve.

Returns:

(undocumented)

fMeasureByThreshold

Dataset<Row> fMeasureByThreshold()

Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.

Returns:

(undocumented)

pr

Dataset<Row> pr()

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

Returns:

(undocumented)

precisionByThreshold

Dataset<Row> 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

Dataset<Row> 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

Dataset<Row> 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)