Package org.apache.spark.ml.classification

Interface LogisticRegressionSummary

All Superinterfaces:

org.apache.spark.ml.classification.ClassificationSummary, Serializable, org.apache.spark.ml.util.Summary

All Known Subinterfaces:

BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary, LogisticRegressionTrainingSummary

public interface LogisticRegressionSummary
extends org.apache.spark.ml.classification.ClassificationSummary

Abstraction for logistic regression results for a given model.

Method Summary

Modifier and Type	Method	Description
BinaryLogisticRegressionSummary	asBinary()	Convenient method for casting to binary logistic regression summary.
String	featuresCol()	Field in "predictions" which gives the features of each instance as a vector.
String	probabilityCol()	Field in "predictions" which gives the probability of each class as a vector.

Methods inherited from interface org.apache.spark.ml.classification.ClassificationSummary

accuracy, falsePositiveRateByLabel, fMeasureByLabel, fMeasureByLabel, labelCol, labels, org$apache$spark$ml$classification$ClassificationSummary$_setter_$org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics_$eq, org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics, precisionByLabel, predictionCol, predictions, recallByLabel, truePositiveRateByLabel, weightCol, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate

Method Details

asBinary

BinaryLogisticRegressionSummary asBinary()

Convenient method for casting to binary logistic regression summary. This method will throw an Exception if the summary is not a binary summary.

Returns:

(undocumented)

featuresCol

String featuresCol()

Field in "predictions" which gives the features of each instance as a vector.

probabilityCol

String probabilityCol()

Field in "predictions" which gives the probability of each class as a vector.