 
       vectors = [
         window.plot.createFactor 'column', TString, [ columnName ]
-        window.plot.createVector 'mean', TNumber, [ mean ], identity
-        window.plot.createVector 'q1', TNumber, [ q1 ], identity
-        window.plot.createVector 'q2', TNumber, [ q2 ], identity
-        window.plot.createVector 'q3', TNumber, [ q3 ], identity
-        window.plot.createVector 'min', TNumber, [ minimum ], identity
-        window.plot.createVector 'max', TNumber, [ maximum ], identity
+        window.plot.createVector 'mean', TNumber, [ mean ]
+        window.plot.createVector 'q1', TNumber, [ q1 ]
+        window.plot.createVector 'q2', TNumber, [ q2 ]
+        window.plot.createVector 'q3', TNumber, [ q3 ]
+        window.plot.createVector 'min', TNumber, [ minimum ]
+        window.plot.createVector 'max', TNumber, [ maximum ]
       ]
 
       window.plot.createFrame 'summary', vectors, (sequence 1), null, 


      vectors = [
        window.plot.createFactor 'characteristic', TString, characteristicData
        window.plot.createVector 'count', TNumber, countData, identity
        window.plot.createVector 'percent', TNumber, percentData, identity
      ]

      window.plot.createFrame 'characteristics', vectors, (sequence characteristicData.length), null,

    description: 'Make a prediction'
    icon: 'bolt'

parseNaNs = (array) ->
  for element in array
    if element is 'NaN' then undefined else element

computeTruePositiveRate = (cm) ->
  [[tn, fp], [fn, tp]] = cm
  tp / (tp + fn)
