Package org.apache.spark.ml.classification

Class BinaryLogisticRegressionSummaryImpl

Object

org.apache.spark.ml.classification.LogisticRegressionSummaryImpl

org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl

All Implemented Interfaces:

Serializable, BinaryClassificationSummary, BinaryLogisticRegressionSummary, ClassificationSummary, LogisticRegressionSummary, scala.Serializable

Direct Known Subclasses:

BinaryLogisticRegressionTrainingSummaryImpl

public class BinaryLogisticRegressionSummaryImpl
extends LogisticRegressionSummaryImpl
implements BinaryLogisticRegressionSummary

Binary logistic regression results for a given model.

param: predictions dataframe output by the model's transform method. param: probabilityCol field in "predictions" which gives the probability of each class as a vector. param: predictionCol field in "predictions" which gives the prediction of each class as a double. param: labelCol field in "predictions" which gives the true label of each instance. param: featuresCol field in "predictions" which gives the features of each instance as a vector. param: weightCol field in "predictions" which gives the weight of each instance.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
BinaryLogisticRegressionSummaryImpl(Dataset<Row> predictions, String probabilityCol, String predictionCol, String labelCol, String featuresCol, String weightCol)

Method Summary

Modifier and Type	Method	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.

Methods inherited from class org.apache.spark.ml.classification.LogisticRegressionSummaryImpl

featuresCol, labelCol, predictionCol, predictions, probabilityCol, weightCol

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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

scoreCol

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

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

asBinary, featuresCol, probabilityCol

Constructor Details

BinaryLogisticRegressionSummaryImpl

public BinaryLogisticRegressionSummaryImpl(Dataset<Row> predictions,
 String probabilityCol,
 String predictionCol,
 String labelCol,
 String featuresCol,
 String weightCol)

Method Details

areaUnderROC

public double areaUnderROC()

Description copied from interface: BinaryClassificationSummary

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

Specified by:

areaUnderROC in interface BinaryClassificationSummary

Returns:

(undocumented)

fMeasureByThreshold

public Dataset<Row> fMeasureByThreshold()

Description copied from interface: BinaryClassificationSummary

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

Specified by:

fMeasureByThreshold in interface BinaryClassificationSummary

Returns:

(undocumented)

pr

public Dataset<Row> pr()

Description copied from interface: BinaryClassificationSummary

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

Specified by:

pr in interface BinaryClassificationSummary

Returns:

(undocumented)

precisionByThreshold

public Dataset<Row> precisionByThreshold()

Description copied from interface: BinaryClassificationSummary

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.

Specified by:

precisionByThreshold in interface BinaryClassificationSummary

Returns:

(undocumented)

recallByThreshold

public Dataset<Row> recallByThreshold()

Description copied from interface: BinaryClassificationSummary

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.

Specified by:

recallByThreshold in interface BinaryClassificationSummary

Returns:

(undocumented)

roc

public Dataset<Row> roc()

Description copied from interface: BinaryClassificationSummary

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

Specified by:

roc in interface BinaryClassificationSummary

Returns:

(undocumented)