Review Evaluation Results

View a detailed cross-validation R² history summary of all output responses in a tabular format using the R² History tool.

1. From the Evaluate step, click the R² Plot tab.
2. From the Channel selector, select the channels to display in the table.
3. Analyze the iteration history summary.

   
   
   Figure 1.

Plot the iteration history of cross-validation R² value for each output response in a 2D chart using the R² Plot tab.

1. From the Evaluate step, click the R² Plot tab.
2. From the Channel selector, select the output response to plot R² along the y-axis.
   The x-axis respresents the iteration history.
3. Analyze the plot.

View a detailed summary of all input variable and output response run data in a tabular format from the Summary post processingEvaluation Data tab.

1. From the Evaluate step, click the Evaluation Data tab.
2. From the Post Processing step, click the Summary tab.
3. From the Channel selector, select the channels to display in the summary table.
4. Analyze the run data summary.
5. Optional: Disable run data from post processing without deleting it entirely from the study by clearing a run's corresponding checkbox in the Post Process column.
   When a run is disabled, it will be removed from all plots, tables, and calculations in the Post Processing step.

Figure 2.

Plot a 2D chart of the input variable and output response values for each run using the Evaluation Plot tool.

1. From the Evaluate step, click the Evaluation Plot tab.
2. From the Channel selector, select the input variable and/or output response to plot along the y-axis.
   The x-axis represents the run numbers.
3. Analyze the plot.

Figure 3.

Analyze the dependency between two sets of data in a scatter plot from the Scatter post processingEvaluation Scatter tab. Visually emphasize data in the scatter plot by appending additional dimensions in the form of bubbles.

1. From the Evaluate Step, click the Evaluation Scatter tab.
2. From the Post Processing step, click the Scatter tab.
3. Select data to display in the scatter plot.
   • Use the Channel selector to select two dimensions of data to plot.

     
     
     Figure 4.

   • Use the Correlation selector to select one or more values from the correlation map to plot.

     Correlation measures the strength and direction between associated variables. Correlation coefficients can have a value from -1 to 1; -1 indicates a strong but negative correlation and 1 indicates a strong and positive correlation.

     Note: Data points are colored according to their corresponding cell in the correlation map when there are no selections active in the Bubbles selector.

     
     
     
     
     Figure 5.

   • Use the Bubbles selector to select additional dimensions of data to visually emphasize in the scatter plot. The selected input variables/output responses are represented by varying sizes and colors of bubbles.
     The size and color of bubbles is determined by values in the run data for the selected input variable/output response. For size, larger bubbles equal larger values. For color, different shades of red, blue, and gray are used to visualize the range of values. The darker the shade of red, the larger the value. The lighter the shade of blue, the smaller the value. Gray represents the median value.

     
     
     Figure 6.

4. Analyze the dependencies between the selected data sets.

Tip: Display selected data in a single plot or separate plots by switching the Multiplot option between (single plot) and (multiple plots).

Configure the scatter plot's display settings by clicking (located in the top, right corner of the work area). For more information about these settings, refer to Evaluation Scatter Tab Settings.