sealed trait FMClassificationTrainingSummary extends FMClassificationSummary with TrainingSummary
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Abstract Value Members
- 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
transformmethod.Dataframe output by the model's
transformmethod.- Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
- 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
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
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- 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")
- final def asInstanceOf[T0]: T0
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): 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
- Annotations
- @Since("3.1.0")
- def fMeasureByLabel(beta: Double): Array[Double]
Returns f-measure for each label (category).
Returns f-measure for each label (category).
- Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
- 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
- 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")
- final def getClass(): Class[_ <: AnyRef]
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- @IntrinsicCandidate() @native()
- def hashCode(): Int
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- @IntrinsicCandidate() @native()
- final def isInstanceOf[T0]: Boolean
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- 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, ..., 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.
- Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
- 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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- @IntrinsicCandidate() @native()
- 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()
- 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
- 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(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
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- @throws(classOf[java.lang.InterruptedException])
- def weightedFMeasure: Double
Returns weighted averaged f1-measure.
Returns weighted averaged f1-measure.
- Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
- def weightedFMeasure(beta: Double): Double
Returns weighted averaged f-measure.
Returns weighted averaged f-measure.
- 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 f-measure)
- 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 f-measure)
- Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
Deprecated Value Members
- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)