Output | Predictive Modeling | Charts by Test Set

The Charts by Test Set tab is shown below:The Charts by Test Set tab contains the following elements:

• Drill-down to plot and compare Receiver Operating Characteristics (ROC) Curves: Use this button to compare ROC curves from models selected in the bar charts. Just click on one or more of the bars to select the models that you want to examine and click to generate the plots.

• Line Graphs: Two plots are shown, one comparing Average Square Error (ASE) and Mean Absolute Error (MAE) and the other comparing Accuracy and Area Under the Curve ((AUC).On both plots, the x-axis is divided into sections for each model compared, and the y-axis is the performance metric.The first plot (left) shows both the average square error and mean absolute error for each model across each run. The smaller the errors, the better the model is at predicting the response.The second plot (right) shows accuracy and area under the curve (AUC) statistics for each of the predictive models. Accuracy is a measure of the proportion of the test set samples that are predicted correctly. The AUC is the area found below the Receiver Operating Characteristics (ROC) curve, which plots true-positive predictions versus false-positive predictions for a binary-response variable. The greater the AUC, the better the model is at predicting true-positive responses.