src/core/metrics/ClassificationMetric.java
public double classificationRecall() {
        if (averageType == AverageType.MACRO) {
            double average = 0;
            int averageCount = 0;
            for (Integer feature : getFeatures()) {
                double TP = getTP(feature);
                double FN = getFN(feature);
                if (TP + FN > 0) {
                    average += TP / (TP + FN);
                    averageCount++;
                }
            }
            return averageCount == 0 ? 0 : average / (double)averageCount;
        }
        else {
            return (double)(getTPTotal()) / (double)(getTPTotal() + getFNTotal());
        }
    }
/**
     * Returns classification recall (sensitivity, hit rate, true positive rate).<br>
     * Recall is calculated as TP / (TP + FN).<br>
     * Measures share of correctly classified positive samples out of all samples actually positive.<br>
     * Takes into consideration if statistics is defined as macro or micro average.<br>
     *
     * @return classification recall.
     */
Returns classification recall (sensitivity, hit rate, true positive rate).
Recall is calculated as TP / (TP + FN).
Measures share of correctly classified positive samples out of all samples actually positive.
Takes into consideration if statistics is defined as macro or micro average.

@return classification recall.
Returns classification recall ( sensitivity hit rate true positive rate ) . Recall is calculated as TP / ( TP + FN ) . Measures share of correctly classified positive samples out of all samples actually positive . Takes into consideration if statistics is defined as macro or micro average . @return classification recall .
public double classificationRecall() {
        if (averageType == AverageType.MACRO) {
            double average = 0;
            int averageCount = 0;
            for (Integer feature : getFeatures()) {
                double TP = getTP(feature);
                double FN = getFN(feature);
                if (TP + FN > 0) {
                    average += TP / (TP + FN);
                    averageCount++;
                }
            }
            return averageCount == 0 ? 0 : average / (double)averageCount;
        }
        else {
            return (double)(getTPTotal()) / (double)(getTPTotal() + getFNTotal());
        }
    }
public double classificationRecall ( ) { if ( averageType == AverageType . MACRO ) { double average = 0 ; int averageCount = 0 ; for ( Integer feature : getFeatures ( ) ) { double TP = getTP ( feature ) ; double FN = getFN ( feature ) ; if ( TP + FN > 0 ) { average += TP / ( TP + FN ) ; averageCount ++ ; } } return averageCount == 0 ? 0 : average / ( double ) averageCount ; } else { return ( double ) ( getTPTotal ( ) ) / ( double ) ( getTPTotal ( ) + getFNTotal ( ) ) ; } }
Returns classification recall (sensitivity, hit rate, true positive rate).<br>
 Recall is calculated as TP / (TP + FN).<br>
 Measures share of correctly classified positive samples out of all samples actually positive.<br>
 Takes into consideration if statistics is defined as macro or micro average.<br>
Returns classification recall ( sensitivity hit rate true positive rate ) . <br > Recall is calculated as TP / ( TP + FN ) . <br > Measures share of correctly classified positive samples out of all samples actually positive . <br > Takes into consideration if statistics is defined as macro or micro average . <br >