org.apache.spark.ml.classification

Class LogisticRegressionTrainingSummaryImpl

Object

org.apache.spark.ml.classification.LogisticRegressionSummaryImpl

org.apache.spark.ml.classification.LogisticRegressionTrainingSummaryImpl

All Implemented Interfaces:

java.io.Serializable, LogisticRegressionSummary, LogisticRegressionTrainingSummary

public class LogisticRegressionTrainingSummaryImpl
extends LogisticRegressionSummaryImpl
implements LogisticRegressionTrainingSummary

Multiclass logistic regression training results.

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. param: objectiveHistory objective function (scaled loss + regularization) at each iteration.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionTrainingSummaryImpl(Dataset<Row> predictions, String probabilityCol, String predictionCol, String labelCol, String featuresCol, double[] objectiveHistory)

Method Summary

Modifier and Type	Method and Description
double[]	objectiveHistory() objective function (scaled loss + regularization) at each iteration.

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

featuresCol, labelCol, predictionCol, predictions, probabilityCol

Methods inherited from class Object

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

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

totalIterations

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

accuracy, asBinary, falsePositiveRateByLabel, featuresCol, fMeasureByLabel, fMeasureByLabel, labelCol, labels, multiclassMetrics, precisionByLabel, predictionCol, predictions, probabilityCol, recallByLabel, truePositiveRateByLabel, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate

Constructor Detail

LogisticRegressionTrainingSummaryImpl

public LogisticRegressionTrainingSummaryImpl(Dataset<Row> predictions,
                                             String probabilityCol,
                                             String predictionCol,
                                             String labelCol,
                                             String featuresCol,
                                             double[] objectiveHistory)

Method Detail

objectiveHistory

public double[] objectiveHistory()

Description copied from interface: LogisticRegressionTrainingSummary

objective function (scaled loss + regularization) at each iteration.

Specified by:

objectiveHistory in interface LogisticRegressionTrainingSummary