 
     when 'deeplearning'
       if table = _.inspect 'output - training_metrics - Metrics for Thresholds', _model
-        renderPlot 'ROC Curve - Training Metrics', no, _.plot (g) ->
-          g(
-            g.path g.position 'fpr', 'tpr'
-            g.line(
-              g.position (g.value 1), (g.value 0)
-              g.strokeColor g.value 'red'
-            )
-            g.from table
-            g.domainX_HACK 0, 1
-            g.domainY_HACK 0, 1
-          )
+        plotter = _.plot (g) ->
+          g(
+            g.path g.position 'fpr', 'tpr'
+            g.line(
+              g.position (g.value 1), (g.value 0)
+              g.strokeColor g.value 'red'
+            )
+            g.from table
+            g.domainX_HACK 0, 1
+            g.domainY_HACK 0, 1
+          )
+        # TODO Mega-hack alert. Last arg thresholdsAndCriteria applicable only to ROC charts for binomial models.
+        renderPlot 'ROC Curve - Training Metrics', no, plotter, getThresholdsAndCriteria _model, 'output - training_metrics - Maximum Metrics'
 
       if table = _.inspect 'output - validation_metrics - Metrics for Thresholds', _model
-        renderPlot 'ROC Curve - Validation Metrics', no, _.plot (g) ->
-          g(
-            g.path g.position 'fpr', 'tpr'
-            g.line(
-              g.position (g.value 1), (g.value 0)
-              g.strokeColor g.value 'red'
-            )
-            g.from table
-            g.domainX_HACK 0, 1
-            g.domainY_HACK 0, 1
-          )
+        plotter = _.plot (g) ->
+          g(
+            g.path g.position 'fpr', 'tpr'
+            g.line(
+              g.position (g.value 1), (g.value 0)
+              g.strokeColor g.value 'red'
+            )
+            g.from table
+            g.domainX_HACK 0, 1
+            g.domainY_HACK 0, 1
+          )
+        # TODO Mega-hack alert. Last arg thresholdsAndCriteria applicable only to ROC charts for binomial models.
+        renderPlot 'ROC Curve - Validation Metrics', no, plotter, getThresholdsAndCriteria _model, 'output - validation_metrics - Maximum Metrics'
 
       if table = _.inspect 'output - Variable Importances', _model
         renderPlot 'Variable Importances', no, _.plot (g) ->

          )

        # TODO Mega-hack alert. Last arg thresholdsAndCriteria applicable only to ROC charts for binomial models.
        renderPlot 'ROC Curve - Training Metrics', no, plotter, getThresholdsAndCriteria _model

      if table = _.inspect 'output - validation_metrics - Metrics for Thresholds', _model
        renderPlot 'ROC Curve - Validation Metrics', no, _.plot (g) ->
          g(
            g.path g.position 'fpr', 'tpr'
            g.line(
              g.position (g.value 1), (g.value 0)
              g.strokeColor g.value 'red'
            )
            g.from table
            g.domainX_HACK 0, 1
            g.domainY_HACK 0, 1
          )

      if table = _.inspect 'output - Variable Importances', _model
        renderPlot 'Variable Importances', no, _.plot (g) ->

    isModified: default_value is actual_value


  getThresholdsAndCriteria = (model, tableName) ->
    if criterionTable = _.inspect tableName, _model

      # Threshold dropdown items
      thresholdVector = table.schema.threshold
