sealed trait RandomForestClassificationSummary extends ClassificationSummary
Abstraction for multiclass RandomForestClassification results for a given model.
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Abstract Value Members
-   abstract  def labelCol: StringField 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 predictionCol: StringField 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: DataFrameDataframe output by the model's transformmethod.Dataframe output by the model's transformmethod.- Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-   abstract  def weightCol: StringField 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
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-   final  def ##: Int- Definition Classes
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-   final  def ==(arg0: Any): Boolean- Definition Classes
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-    def accuracy: DoubleReturns 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")
 
-    def asBinary: BinaryRandomForestClassificationSummaryConvenient method for casting to BinaryRandomForestClassificationSummary. Convenient method for casting to BinaryRandomForestClassificationSummary. This method will throw an Exception if the summary is not a binary summary. - Annotations
- @Since("3.1.0")
 
-   final  def asInstanceOf[T0]: T0- Definition Classes
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-    def clone(): AnyRef- Attributes
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
 
-   final  def eq(arg0: AnyRef): Boolean- Definition Classes
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-    def equals(arg0: AnyRef): Boolean- Definition Classes
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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")
 
-    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]- Definition Classes
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- Annotations
- @IntrinsicCandidate() @native()
 
-    def hashCode(): Int- Definition Classes
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- Annotations
- @IntrinsicCandidate() @native()
 
-   final  def isInstanceOf[T0]: Boolean- Definition Classes
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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. - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-   final  def ne(arg0: AnyRef): Boolean- Definition Classes
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-   final  def notify(): Unit- Definition Classes
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- Annotations
- @IntrinsicCandidate() @native()
 
-   final  def notifyAll(): Unit- Definition Classes
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- Annotations
- @IntrinsicCandidate() @native()
 
-    def precisionByLabel: Array[Double]Returns precision for each label (category). Returns precision for each label (category). - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-    def recallByLabel: Array[Double]Returns recall for each label (category). Returns recall for each label (category). - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-   final  def synchronized[T0](arg0: => T0): T0- Definition Classes
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-    def toString(): String- Definition Classes
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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")
 
-   final  def wait(arg0: Long, arg1: Int): Unit- Definition Classes
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- Annotations
- @throws(classOf[java.lang.InterruptedException])
 
-   final  def wait(arg0: Long): Unit- Definition Classes
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- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
 
-   final  def wait(): Unit- Definition Classes
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- Annotations
- @throws(classOf[java.lang.InterruptedException])
 
-    def weightedFMeasure: DoubleReturns weighted averaged f1-measure. Returns weighted averaged f1-measure. - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-    def weightedFMeasure(beta: Double): DoubleReturns weighted averaged f-measure. Returns weighted averaged f-measure. - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-    def weightedFalsePositiveRate: DoubleReturns weighted false positive rate. Returns weighted false positive rate. - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-    def weightedPrecision: DoubleReturns weighted averaged precision. Returns weighted averaged precision. - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-    def weightedRecall: DoubleReturns weighted averaged recall. Returns weighted averaged recall. (equals to precision, recall and f-measure) - Definition Classes
- ClassificationSummary
- Annotations
- @Since("3.1.0")
 
-    def weightedTruePositiveRate: DoubleReturns 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- Attributes
- protected[lang]
- Definition Classes
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
- (Since version 9)