               )
               g.from table
             )
+
+      if table = _.inspect 'output - training_metrics - Metrics for Thresholds', _model
+        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#{getAucAsLabel _model, 'output - training_metrics'}", no, plotter, getThresholdsAndCriteria _model, 'output - training_metrics - Maximum Metrics'
+
+      if table = _.inspect 'output - validation_metrics - Metrics for Thresholds', _model
+        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#{getAucAsLabel _model, 'output - validation_metrics'}", no, plotter, getThresholdsAndCriteria _model, 'output - validation_metrics - Maximum Metrics'
+
+      if table = _.inspect 'output - cross_validation_metrics - Metrics for Thresholds', _model
+        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 - Cross Validation Metrics#{getAucAsLabel _model, 'output - cross_validation_metrics'}", no, plotter, getThresholdsAndCriteria _model, 'output - cross_validation_metrics - Maximum Metrics'
+
+      if table = _.inspect 'output - Standardized Coefficient Magnitudes', _model
+        renderPlot 'Standardized Coefficient Magnitudes', no, _.plot (g) ->
+          g(
+            g.rect(
+              g.position 'coefficients', 'names'
+              g.fillColor 'sign'
+            )
+            g.from table
+            g.limit 25
+          )
 
       if output = _model.output
         if output.model_category is 'Multinomial'

          )

    when 'glm'
      if table = _.inspect 'output - training_metrics - Metrics for Thresholds', _model
        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#{getAucAsLabel _model, 'output - training_metrics'}", no, plotter, getThresholdsAndCriteria _model, 'output - training_metrics - Maximum Metrics'

      if table = _.inspect 'output - validation_metrics - Metrics for Thresholds', _model
        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#{getAucAsLabel _model, 'output - validation_metrics'}", no, plotter, getThresholdsAndCriteria _model, 'output - validation_metrics - Maximum Metrics'

      if table = _.inspect 'output - cross_validation_metrics - Metrics for Thresholds', _model
        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 - Cross Validation Metrics#{getAucAsLabel _model, 'output - cross_validation_metrics'}", no, plotter, getThresholdsAndCriteria _model, 'output - cross_validation_metrics - Maximum Metrics'

      if table = _.inspect 'output - Standardized Coefficient Magnitudes', _model
        renderPlot 'Standardized Coefficient Magnitudes', no, _.plot (g) ->
          g(
            g.rect(
              g.position 'coefficients', 'names'
              g.fillColor 'sign'
            )
            g.from table
            g.limit 25
          )

      if table = _.inspect 'output - Scoring History', _model
        lambdaSearchParameter = find _model.parameters, (parameter) -> parameter.name is 'lambda_search'
      

            renderMultinomialConfusionMatrix 'Cross Validation Metrics - Confusion Matrix', confusionMatrix

    when 'deeplearning'
      if table = _.inspect 'output - Scoring History', _model
        if table.schema['validation_logloss'] and table.schema['training_logloss']
          renderPlot 'Scoring History - logloss', no, _.plot (g) ->
