code: 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);
  
      }
docs: 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