class MultilabelMetrics extends AnyRef
Evaluator for multilabel classification.
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val
accuracy: Double
Returns accuracy
Returns accuracy
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def
f1Measure(label: Double): Double
Returns f1-measure for a given label (category)
Returns f1-measure for a given label (category)
- label
the label.
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val
f1Measure: Double
Returns document-based f1-measure averaged by the number of documents
Returns document-based f1-measure averaged by the number of documents
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val
hammingLoss: Double
Returns Hamming-loss
Returns Hamming-loss
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lazy val
labels: Array[Double]
Returns the sequence of labels in ascending order
Returns the sequence of labels in ascending order
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lazy val
microF1Measure: Double
Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)
Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)
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lazy val
microPrecision: Double
Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)
Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)
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lazy val
microRecall: Double
Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)
Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)
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def
precision(label: Double): Double
Returns precision for a given label (category)
Returns precision for a given label (category)
- label
the label.
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val
precision: Double
Returns document-based precision averaged by the number of documents
Returns document-based precision averaged by the number of documents
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def
recall(label: Double): Double
Returns recall for a given label (category)
Returns recall for a given label (category)
- label
the label.
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val
recall: Double
Returns document-based recall averaged by the number of documents
Returns document-based recall averaged by the number of documents
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val
subsetAccuracy: Double
Returns subset accuracy (for equal sets of labels)
Returns subset accuracy (for equal sets of labels)
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