Package weka.classifiers

Examples of weka.classifiers.Evaluation.weightedRecall()

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    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());
    result[current++] = new Double(eval.weightedTrueNegativeRate());
    result[current++] = new Double(eval.weightedFalseNegativeRate());
    result[current++] = new Double(eval.weightedPrecision());
    result[current++] = new Double(eval.weightedRecall());
    result[current++] = new Double(eval.weightedFMeasure());
    result[current++] = new Double(eval.weightedAreaUnderROC());
   
    // Unweighted IR stats
    result[current++] = new Double(eval.unweightedMacroFmeasure());
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    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());
    result[current++] = new Double(eval.weightedTrueNegativeRate());
    result[current++] = new Double(eval.weightedFalseNegativeRate());
    result[current++] = new Double(eval.weightedPrecision());
    result[current++] = new Double(eval.weightedRecall());
    result[current++] = new Double(eval.weightedFMeasure());
    result[current++] = new Double(eval.weightedAreaUnderROC());
   
    // Timing stats
    result[current++] = new Double(trainTimeElapsed / 1000.0);
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    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());
    result[current++] = new Double(eval.weightedTrueNegativeRate());
    result[current++] = new Double(eval.weightedFalseNegativeRate());
    result[current++] = new Double(eval.weightedPrecision());
    result[current++] = new Double(eval.weightedRecall());
    result[current++] = new Double(eval.weightedFMeasure());
    result[current++] = new Double(eval.weightedAreaUnderROC());
   
    // Unweighted IR stats
    result[current++] = new Double(eval.unweightedMacroFmeasure());
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