org.apache.spark.ml.classification

Class LogisticRegressionSummaryImpl

Object

org.apache.spark.ml.classification.LogisticRegressionSummaryImpl

All Implemented Interfaces:

java.io.Serializable, LogisticRegressionSummary

Direct Known Subclasses:

BinaryLogisticRegressionSummaryImpl, LogisticRegressionTrainingSummaryImpl

public class LogisticRegressionSummaryImpl
extends Object
implements LogisticRegressionSummary

Multiclass 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 for a data instance 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.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionSummaryImpl(Dataset<Row> predictions, String probabilityCol, String predictionCol, String labelCol, String featuresCol)

Method Summary

Modifier and Type	Method and Description
String	featuresCol() Field in "predictions" which gives the features of each instance as a vector.
String	labelCol() Field in "predictions" which gives the true label of each instance (if available).
String	predictionCol() Field in "predictions" which gives the prediction of each class.
Dataset<Row>	predictions() Dataframe output by the model's transform method.
String	probabilityCol() Field in "predictions" which gives the probability of each class as a vector.

Methods inherited from class Object

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

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

accuracy, asBinary, falsePositiveRateByLabel, fMeasureByLabel, fMeasureByLabel, labels, multiclassMetrics, precisionByLabel, recallByLabel, truePositiveRateByLabel, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate

Constructor Detail

LogisticRegressionSummaryImpl

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

Method Detail

featuresCol

public 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

labelCol

public String labelCol()

Description copied from interface: LogisticRegressionSummary

Field in "predictions" which gives the true label of each instance (if available).

Specified by:

labelCol in interface LogisticRegressionSummary

predictionCol

public String predictionCol()

Description copied from interface: LogisticRegressionSummary

Field in "predictions" which gives the prediction of each class.

Specified by:

predictionCol in interface LogisticRegressionSummary

predictions

public Dataset<Row> predictions()

Description copied from interface: LogisticRegressionSummary

Dataframe output by the model's transform method.

Specified by:

predictions in interface LogisticRegressionSummary

Returns:

(undocumented)

probabilityCol

public String probabilityCol()

Description copied from interface: LogisticRegressionSummary

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

Specified by:

probabilityCol in interface LogisticRegressionSummary