sealed trait LinearSVCSummary extends BinaryClassificationSummary
Abstraction for LinearSVC results for a given model.
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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
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- @Since( "3.1.0" )
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abstract
def
predictionCol: String
Field in "predictions" which gives the prediction of each class.
Field in "predictions" which gives the prediction of each class.
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- ClassificationSummary
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- @Since( "3.1.0" )
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abstract
def
predictions: DataFrame
Dataframe output by the model's
transform
method.Dataframe output by the model's
transform
method.- Definition Classes
- ClassificationSummary
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- @Since( "3.1.0" )
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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
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- @Since( "3.1.0" )
Concrete Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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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
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- @Since( "3.1.0" )
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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
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- @Since( "3.1.0" )
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
fMeasureByLabel: Array[Double]
Returns f1-measure for each label (category).
Returns f1-measure for each label (category).
- Definition Classes
- ClassificationSummary
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- @Since( "3.1.0" )
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def
fMeasureByLabel(beta: Double): Array[Double]
Returns f-measure for each label (category).
Returns f-measure for each label (category).
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- ClassificationSummary
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- @Since( "3.1.0" )
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lazy val
fMeasureByThreshold: DataFrame
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
- Definition Classes
- BinaryClassificationSummary
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- @Since( "3.1.0" ) @transient()
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def
falsePositiveRateByLabel: Array[Double]
Returns false positive rate for each label (category).
Returns false positive rate for each label (category).
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- ClassificationSummary
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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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, ..., numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.
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- ClassificationSummary
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- @Since( "3.1.0" )
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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lazy val
pr: DataFrame
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Returns the precision-recall 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()
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def
precisionByLabel: Array[Double]
Returns precision for each label (category).
Returns precision for each label (category).
- Definition Classes
- ClassificationSummary
- Annotations
- @Since( "3.1.0" )
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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()
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def
recallByLabel: Array[Double]
Returns recall for each label (category).
Returns recall for each label (category).
- Definition Classes
- ClassificationSummary
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- @Since( "3.1.0" )
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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()
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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()
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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
- BinaryClassificationSummary
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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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" )
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
weightedFMeasure: Double
Returns weighted averaged f1-measure.
Returns weighted averaged f1-measure.
- Definition Classes
- ClassificationSummary
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- @Since( "3.1.0" )
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def
weightedFMeasure(beta: Double): Double
Returns weighted averaged f-measure.
Returns weighted averaged f-measure.
- Definition Classes
- ClassificationSummary
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- @Since( "3.1.0" )
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def
weightedFalsePositiveRate: Double
Returns weighted false positive rate.
Returns weighted false positive rate.
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- ClassificationSummary
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- @Since( "3.1.0" )
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def
weightedPrecision: Double
Returns weighted averaged precision.
Returns weighted averaged precision.
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- ClassificationSummary
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- @Since( "3.1.0" )
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def
weightedRecall: Double
Returns weighted averaged recall.
Returns weighted averaged recall. (equals to precision, recall and f-measure)
- Definition Classes
- ClassificationSummary
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- @Since( "3.1.0" )
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def
weightedTruePositiveRate: Double
Returns weighted true positive rate.
Returns weighted true positive rate. (equals to precision, recall and f-measure)
- Definition Classes
- ClassificationSummary
- Annotations
- @Since( "3.1.0" )