Package org.apache.spark.ml.classification

Class LogisticRegressionTrainingSummaryImpl

Object

org.apache.spark.ml.classification.LogisticRegressionSummaryImpl

org.apache.spark.ml.classification.LogisticRegressionTrainingSummaryImpl

All Implemented Interfaces:

Serializable, ClassificationSummary, LogisticRegressionSummary, LogisticRegressionTrainingSummary, TrainingSummary, Summary

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: weightCol field in "predictions" which gives the weight of each instance. param: objectiveHistory objective function (scaled loss + regularization) at each iteration.

See Also:

• Serialized Form

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method	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, 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.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

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

totalIterations

Constructor Details

LogisticRegressionTrainingSummaryImpl

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

Method Details

objectiveHistory

public double[] objectiveHistory()

Description copied from interface: TrainingSummary

objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.

Specified by:

objectiveHistory in interface TrainingSummary

Returns:

(undocumented)