public class BinaryLogisticRegressionTrainingSummaryImpl extends BinaryLogisticRegressionSummaryImpl implements BinaryLogisticRegressionTrainingSummary
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.
Constructor and Description |
---|
BinaryLogisticRegressionTrainingSummaryImpl(Dataset<Row> predictions,
String probabilityCol,
String predictionCol,
String labelCol,
String featuresCol,
String weightCol,
double[] objectiveHistory) |
Modifier and Type | Method and Description |
---|---|
double[] |
objectiveHistory()
objective function (scaled loss + regularization) at each iteration.
|
areaUnderROC, fMeasureByThreshold, pr, precisionByThreshold, recallByThreshold, roc
featuresCol, labelCol, predictionCol, predictions, probabilityCol, weightCol
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
scoreCol
areaUnderROC, fMeasureByThreshold, pr, precisionByThreshold, recallByThreshold, roc
asBinary, featuresCol, probabilityCol
accuracy, falsePositiveRateByLabel, fMeasureByLabel, fMeasureByLabel, labelCol, labels, precisionByLabel, predictionCol, predictions, recallByLabel, truePositiveRateByLabel, weightCol, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate
totalIterations
public double[] objectiveHistory()
TrainingSummary
objectiveHistory
in interface TrainingSummary