sealed trait LogisticRegressionSummary extends Serializable
:: Experimental :: Abstraction for logistic regression results for a given model.
Currently, the summary ignores the instance weights.
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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.
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- @Since( "1.6.0" )
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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).
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- @Since( "1.5.0" )
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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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- @Since( "2.3.0" )
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abstract
def
predictions: DataFrame
Dataframe output by the model's
transformmethod.Dataframe output by the model's
transformmethod.- Annotations
- @Since( "1.5.0" )
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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.
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- @Since( "1.5.0" )
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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.)
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- @Since( "2.3.0" )
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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.
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- @Since( "2.3.0" )
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asInstanceOf[T0]: T0
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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).
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- @Since( "2.3.0" )
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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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- @Since( "2.3.0" )
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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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finalize(): Unit
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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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- @Since( "2.3.0" )
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def
precisionByLabel: Array[Double]
Returns precision for each label (category).
Returns precision for each label (category).
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- @Since( "2.3.0" )
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def
recallByLabel: Array[Double]
Returns recall for each label (category).
Returns recall for each label (category).
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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).
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final
def
wait(): Unit
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def
wait(arg0: Long): Unit
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def
weightedFMeasure: Double
Returns weighted averaged f1-measure.
Returns weighted averaged f1-measure.
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- @Since( "2.3.0" )
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def
weightedFMeasure(beta: Double): Double
Returns weighted averaged f-measure.
Returns weighted averaged f-measure.
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- @Since( "2.3.0" )
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def
weightedFalsePositiveRate: Double
Returns weighted false positive rate.
Returns weighted false positive rate.
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- @Since( "2.3.0" )
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def
weightedPrecision: Double
Returns weighted averaged precision.
Returns weighted averaged precision.
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- @Since( "2.3.0" )
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def
weightedRecall: Double
Returns weighted averaged recall.
Returns weighted averaged recall. (equals to precision, recall and f-measure)
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- @Since( "2.3.0" )
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def
weightedTruePositiveRate: Double
Returns weighted true positive rate.
Returns weighted true positive rate. (equals to precision, recall and f-measure)
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- @Since( "2.3.0" )