org.apache.spark.ml.classification

Class BinaryLogisticRegressionSummary

java.lang.Object

org.apache.spark.ml.classification.BinaryLogisticRegressionSummary

All Implemented Interfaces:

java.io.Serializable, LogisticRegressionSummary

Direct Known Subclasses:

BinaryLogisticRegressionTrainingSummary

public class BinaryLogisticRegressionSummary
extends java.lang.Object
implements LogisticRegressionSummary

:: Experimental :: Binary Logistic regression results for a given model. param: predictions dataframe outputted by the model's transform method. param: probabilityCol field in "predictions" which gives the calibrated probability of each instance. 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.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
double	areaUnderROC() Computes the area under the receiver operating characteristic (ROC) curve.
java.lang.String	featuresCol() Field in "predictions" which gives the features of each instance as a vector.
DataFrame	fMeasureByThreshold() Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
java.lang.String	labelCol() Field in "predictions" which gives the true label of each instance.
DataFrame	pr() Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
DataFrame	precisionByThreshold() Returns a dataframe with two fields (threshold, precision) curve.
DataFrame	predictions() Dataframe outputted by the model's `transform` method.
java.lang.String	probabilityCol() Field in "predictions" which gives the calibrated probability of each instance as a vector.
DataFrame	recallByThreshold() Returns a dataframe with two fields (threshold, recall) curve.
DataFrame	roc() Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

Methods inherited from class java.lang.Object

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

Method Detail

predictions

public DataFrame predictions()

Description copied from interface: LogisticRegressionSummary

Dataframe outputted by the model's `transform` method.

Specified by:

predictions in interface LogisticRegressionSummary

probabilityCol

public java.lang.String probabilityCol()

Description copied from interface: LogisticRegressionSummary

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

Specified by:

probabilityCol in interface LogisticRegressionSummary

labelCol

public java.lang.String labelCol()

Description copied from interface: LogisticRegressionSummary

Field in "predictions" which gives the true label of each instance.

Specified by:

labelCol in interface LogisticRegressionSummary

featuresCol

public java.lang.String featuresCol()

Description copied from interface: LogisticRegressionSummary

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

Specified by:

featuresCol in interface LogisticRegressionSummary

roc

public DataFrame roc()

Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

Returns:

(undocumented)

See Also:

http://en.wikipedia.org/wiki/Receiver_operating_characteristic

areaUnderROC

public double areaUnderROC()

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

Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

Returns:

(undocumented)

pr

public DataFrame pr()

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

Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

Returns:

(undocumented)

fMeasureByThreshold

public DataFrame fMeasureByThreshold()

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

Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

Returns:

(undocumented)

precisionByThreshold

public DataFrame 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.

Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

Returns:

(undocumented)

recallByThreshold

public DataFrame 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.

Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

Returns:

(undocumented)