Comparing the Models
- Click the Run button.
The model nugget is built and placed on the canvas, and also on the Models palette in the upper right corner of the window. You can browse the nugget, or save or deploy it in a number of other ways.
Open the model nugget; it lists details about each of the models created during the run. (In a real situation, in which hundreds of models are estimated on a large dataset, this could take many hours.)
If you want to explore any of the individual models further, you can double-click on a model nugget icon in the Model column to drill down and browse the individual model results; from there you can generate modeling nodes, model nuggets, or evaluation charts.
By default, models are sorted by correlation because this was the measure you selected in the Auto Numeric node. For purposes of ranking, the absolute value of the correlation is used, with values closer to 1 indicating a stronger relationship. The Generalized Linear model ranks best on this measure, but several others are nearly as accurate. The Generalized Linear model also has the lowest relative error.
You can sort on a different column by clicking the header for that column, or you can choose the desired measure from the Sort by list on the toolbar.
Each graph displays a plot of observed values against predicted values for the model, providing a quick visual indication of the correlation between them. For a good model, points should cluster along the diagonal, which is true for all the models in this example.
In the Graph column, you can double-click on a thumbnail to generate a full-sized graph.
Based on these results, you decide to use all three of these most accurate models. By combining predictions from multiple models, limitations in individual models may be avoided, resulting in a higher overall accuracy.
In the Use? column, ensure that all three models are selected.
Attach an Analysis node (Output palette) after the model nugget. Right-click on the Analysis node and choose Run to run the stream.
The averaged score generated by the ensembled model is added in a field named $XR-taxable_value, with a correlation of 0.922, which is higher than those of the three individual models. The ensemble scores also show a low mean absolute error and may perform better than any of the individual models when applied to other datasets.
