server/src/main/resources/eclairjs/mllib/evaluation/BinaryClassificationMetrics.js
function(scoreAndLabels,numBins) {
        var jvmObject;
        if (scoreAndLabels instanceof org.apache.spark.mllib.evaluation.BinaryClassificationMetrics) {
            jvmObject = scoreAndLabels;
        } else {
            jvmObject = new org.apache.spark.mllib.evaluation.BinaryClassificationMetrics(Utils.unwrapObject(scoreAndLabels).rdd(),numBins);
        }

         this.logger = Logger.getLogger("BinaryClassificationMetrics_js");
         JavaWrapper.call(this, jvmObject);

    }
function(scoreAndLabels,numBins) {
        var jvmObject;
        if (scoreAndLabels instanceof org.apache.spark.mllib.evaluation.BinaryClassificationMetrics) {
            jvmObject = scoreAndLabels;
        } else {
            jvmObject = new org.apache.spark.mllib.evaluation.BinaryClassificationMetrics(Utils.unwrapObject(scoreAndLabels).rdd(),numBins);
        }

         this.logger = Logger.getLogger("BinaryClassificationMetrics_js");
         JavaWrapper.call(this, jvmObject);

    }
function ( scoreAndLabels , numBins ) { var jvmObject ; if ( scoreAndLabels instanceof org . apache . spark . mllib . evaluation . BinaryClassificationMetrics ) { jvmObject = scoreAndLabels ; } else { jvmObject = new org . apache . spark . mllib . evaluation . BinaryClassificationMetrics ( Utils . unwrapObject ( scoreAndLabels ) . rdd ( ) , numBins ) ; } this . logger = Logger . getLogger ( "BinaryClassificationMetrics_js" ) ; JavaWrapper . call ( this , jvmObject ) ; }
Evaluator for binary classification.

@param scoreAndLabels an RDD of (score, label) pairs.
@param numBins if greater than 0, then the curves (ROC curve, PR curve) computed internally
will be down-sampled to this many "bins". If 0, no down-sampling will occur.
This is useful because the curve contains a point for each distinct score
in the input, and this could be as large as the input itself -- millions of
points or more, when thousands may be entirely sufficient to summarize
the curve. After down-sampling, the curves will instead be made of approximately
`numBins` points instead. Points are made from bins of equal numbers of
consecutive points. The size of each bin is
`floor(scoreAndLabels.count() / numBins)`, which means the resulting number
of bins may not exactly equal numBins. The last bin in each partition may
be smaller as a result, meaning there may be an extra sample at
partition boundaries.

@classdesc
 
@param {module:eclairjs.RDD} scoreAndLabels
@param {number} numBins
@class
@memberof module:eclairjs/mllib/evaluation
Evaluator for binary classification .