           )
 
     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'
       

                )
                g.from table
              )

      if output = _model.output
        if output.model_category is 'Multinomial'

            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) ->
