Interface LabelEvaluation

All Superinterfaces:
ClassifierEvaluation<Label>, Evaluation<Label>, com.oracle.labs.mlrg.olcut.provenance.Provenancable<EvaluationProvenance>

public interface LabelEvaluation extends ClassifierEvaluation<Label>
Adds multi-class classification specific metrics to ClassifierEvaluation.
  • Method Details

    • accuracy

      double accuracy()
      The overall accuracy of the evaluation.
      Returns:
      The accuracy.
    • accuracy

      double accuracy(Label label)
      The per label accuracy of the evaluation.
      Parameters:
      label - The target label.
      Returns:
      The per label accuracy.
    • AUCROC

      double AUCROC(Label label)
      Area under the ROC curve.
      Parameters:
      label - target label
      Returns:
      AUC ROC score
      Implementation Requirements:
      Implementations of this class are expected to throw UnsupportedOperationException if the model corresponding to this evaluation does not generate probabilities, which are required to compute the ROC curve.
    • averageAUCROC

      double averageAUCROC(boolean weighted)
      Area under the ROC curve averaged across labels.

      If weighted is false, use a macro average, if true, weight by the evaluation's observed class counts.

      Parameters:
      weighted - If true weight by the class counts, if false use a macro average.
      Returns:
      The average AUCROC.
      Implementation Requirements:
      Implementations of this class are expected to throw UnsupportedOperationException if the model corresponding to this evaluation does not generate probabilities, which are required to compute the ROC curve.
    • averagedPrecision

      double averagedPrecision(Label label)
      Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
      Parameters:
      label - The target label.
      Returns:
      The averaged precision for that label.
      See Also:
      Implementation Requirements:
      Implementations of this class are expected to throw UnsupportedOperationException if the model corresponding to this evaluation does not generate probabilities, which are required to compute the ROC curve.
    • precisionRecallCurve

      Calculates the Precision Recall curve for a single label.
      Parameters:
      label - The target label.
      Returns:
      The precision recall curve for that label.
      See Also:
      Implementation Requirements:
      Implementations of this class are expected to throw UnsupportedOperationException if the model corresponding to this evaluation does not generate probabilities, which are required to compute the ROC curve.
    • toHTML

      default String toHTML()
      Returns a HTML formatted String representing this evaluation.
      Returns:
      A HTML formatted String.
    • toFormattedString

      This method produces a nicely formatted String output, with appropriate tabs and newlines, suitable for display on a terminal. It can be used as an implementation of the EvaluationRenderer functional interface.
      Parameters:
      evaluation - The evaluation to format.
      Returns:
      Formatted output showing the main results of the evaluation.
    • toHTML

      static String toHTML(LabelEvaluation evaluation)
      This method produces a HTML formatted String output, with appropriate tabs and newlines, suitable for integation into a webpage. It can be used as an implementation of the EvaluationRenderer functional interface.
      Parameters:
      evaluation - The evaluation to format.
      Returns:
      Formatted HTML output showing the main results of the evaluation.