sealed trait BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary
Abstraction for binary logistic regression training results.
 Source
 LogisticRegression.scala
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 BinaryLogisticRegressionTrainingSummary
 LogisticRegressionTrainingSummary
 TrainingSummary
 BinaryLogisticRegressionSummary
 BinaryClassificationSummary
 LogisticRegressionSummary
 ClassificationSummary
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Abstract Value Members

abstract
def
featuresCol: String
Field in "predictions" which gives the features of each instance as a vector.
Field in "predictions" which gives the features of each instance as a vector.
 Definition Classes
 LogisticRegressionSummary
 Annotations
 @Since( "1.6.0" )

abstract
def
labelCol: String
Field in "predictions" which gives the true label of each instance (if available).
Field in "predictions" which gives the true label of each instance (if available).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

abstract
def
objectiveHistory: Array[Double]
objective function (scaled loss + regularization) at each iteration.
objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.
 Definition Classes
 TrainingSummary
 Annotations
 @Since( "3.1.0" )

abstract
def
predictionCol: String
Field in "predictions" which gives the prediction of each class.
Field in "predictions" which gives the prediction of each class.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

abstract
def
predictions: DataFrame
Dataframe output by the model's
transform
method.Dataframe output by the model's
transform
method. Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

abstract
def
probabilityCol: String
Field in "predictions" which gives the probability of each class as a vector.
Field in "predictions" which gives the probability of each class as a vector.
 Definition Classes
 LogisticRegressionSummary
 Annotations
 @Since( "1.5.0" )

abstract
def
weightCol: String
Field in "predictions" which gives the weight of each instance.
Field in "predictions" which gives the weight of each instance.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )
Concrete Value Members

final
def
!=(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

final
def
##(): Int
 Definition Classes
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final
def
==(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

def
accuracy: Double
Returns accuracy.
Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

lazy val
areaUnderROC: Double
Computes the area under the receiver operating characteristic (ROC) curve.
Computes the area under the receiver operating characteristic (ROC) curve.
 Definition Classes
 BinaryClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
asBinary: BinaryLogisticRegressionSummary
Convenient method for casting to binary logistic regression summary.
Convenient method for casting to binary logistic regression summary. This method will throw an Exception if the summary is not a binary summary.
 Definition Classes
 LogisticRegressionSummary
 Annotations
 @Since( "2.3.0" )

final
def
asInstanceOf[T0]: T0
 Definition Classes
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def
clone(): AnyRef
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 protected[lang]
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 @throws( ... ) @native()

final
def
eq(arg0: AnyRef): Boolean
 Definition Classes
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def
equals(arg0: Any): Boolean
 Definition Classes
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def
fMeasureByLabel: Array[Double]
Returns f1measure for each label (category).
Returns f1measure for each label (category).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
fMeasureByLabel(beta: Double): Array[Double]
Returns fmeasure for each label (category).
Returns fmeasure for each label (category).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

lazy val
fMeasureByThreshold: DataFrame
Returns a dataframe with two fields (threshold, FMeasure) curve with beta = 1.0.
Returns a dataframe with two fields (threshold, FMeasure) curve with beta = 1.0.
 Definition Classes
 BinaryClassificationSummary
 Annotations
 @Since( "3.1.0" ) @transient()

def
falsePositiveRateByLabel: Array[Double]
Returns false positive rate for each label (category).
Returns false positive rate for each label (category).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
finalize(): Unit
 Attributes
 protected[lang]
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 @throws( classOf[java.lang.Throwable] )

final
def
getClass(): Class[_]
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 @native()

def
hashCode(): Int
 Definition Classes
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 @native()

final
def
isInstanceOf[T0]: Boolean
 Definition Classes
 Any

def
labels: Array[Double]
Returns the sequence of labels in ascending order.
Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.
Note: In most cases, it will be values {0.0, 1.0, ..., numClasses1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses1 instead of the expected numClasses.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

final
def
ne(arg0: AnyRef): Boolean
 Definition Classes
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final
def
notify(): Unit
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 @native()

final
def
notifyAll(): Unit
 Definition Classes
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 @native()

lazy val
pr: DataFrame
Returns the precisionrecall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Returns the precisionrecall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
 Definition Classes
 BinaryClassificationSummary
 Annotations
 @Since( "3.1.0" ) @transient()

def
precisionByLabel: Array[Double]
Returns precision for each label (category).
Returns precision for each label (category).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

lazy val
precisionByThreshold: DataFrame
Returns a dataframe with two fields (threshold, precision) curve.
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.
 Definition Classes
 BinaryClassificationSummary
 Annotations
 @Since( "3.1.0" ) @transient()

def
recallByLabel: Array[Double]
Returns recall for each label (category).
Returns recall for each label (category).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

lazy val
recallByThreshold: DataFrame
Returns a dataframe with two fields (threshold, recall) curve.
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.
 Definition Classes
 BinaryClassificationSummary
 Annotations
 @Since( "3.1.0" ) @transient()

lazy val
roc: DataFrame
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic
 Definition Classes
 BinaryClassificationSummary
 Annotations
 @Since( "3.1.0" ) @transient()

def
scoreCol: String
Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
 Definition Classes
 BinaryLogisticRegressionSummary → BinaryClassificationSummary

final
def
synchronized[T0](arg0: ⇒ T0): T0
 Definition Classes
 AnyRef

def
toString(): String
 Definition Classes
 AnyRef → Any

def
totalIterations: Int
Number of training iterations.
Number of training iterations.
 Definition Classes
 TrainingSummary
 Annotations
 @Since( "3.1.0" )

def
truePositiveRateByLabel: Array[Double]
Returns true positive rate for each label (category).
Returns true positive rate for each label (category).
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

final
def
wait(): Unit
 Definition Classes
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 @throws( ... )

final
def
wait(arg0: Long, arg1: Int): Unit
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 @throws( ... )

final
def
wait(arg0: Long): Unit
 Definition Classes
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 @throws( ... ) @native()

def
weightedFMeasure: Double
Returns weighted averaged f1measure.
Returns weighted averaged f1measure.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
weightedFMeasure(beta: Double): Double
Returns weighted averaged fmeasure.
Returns weighted averaged fmeasure.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
weightedFalsePositiveRate: Double
Returns weighted false positive rate.
Returns weighted false positive rate.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
weightedPrecision: Double
Returns weighted averaged precision.
Returns weighted averaged precision.
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
weightedRecall: Double
Returns weighted averaged recall.
Returns weighted averaged recall. (equals to precision, recall and fmeasure)
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )

def
weightedTruePositiveRate: Double
Returns weighted true positive rate.
Returns weighted true positive rate. (equals to precision, recall and fmeasure)
 Definition Classes
 ClassificationSummary
 Annotations
 @Since( "3.1.0" )