org.apache.spark.ml.classification

Class BinaryRandomForestClassificationSummaryImpl

Object

org.apache.spark.ml.classification.RandomForestClassificationSummaryImpl

org.apache.spark.ml.classification.BinaryRandomForestClassificationSummaryImpl

All Implemented Interfaces:

java.io.Serializable, BinaryClassificationSummary, BinaryRandomForestClassificationSummary, ClassificationSummary, RandomForestClassificationSummary

Direct Known Subclasses:

BinaryRandomForestClassificationTrainingSummaryImpl

public class BinaryRandomForestClassificationSummaryImpl
extends RandomForestClassificationSummaryImpl
implements BinaryRandomForestClassificationSummary

Binary RandomForestClassification for a given model.

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BinaryRandomForestClassificationSummaryImpl(Dataset<Row> predictions, String scoreCol, String predictionCol, String labelCol, String weightCol)

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 class org.apache.spark.ml.classification.RandomForestClassificationSummaryImpl

labelCol, predictionCol, predictions, weightCol

Methods inherited from class Object

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

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.RandomForestClassificationSummary

asBinary

Constructor Detail

BinaryRandomForestClassificationSummaryImpl

public BinaryRandomForestClassificationSummaryImpl(Dataset<Row> predictions,
                                                   String scoreCol,
                                                   String predictionCol,
                                                   String labelCol,
                                                   String weightCol)

Method Detail

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)

scoreCol

public String scoreCol()

Description copied from interface: BinaryClassificationSummary

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

Specified by:

scoreCol in interface BinaryClassificationSummary

Returns:

(undocumented)