The (
AUC
is the area found below the

Receiver Operating Characteristics (ROC) Curves
, which plot true-positive predictions versus false-positive predictions for a binary-response

variable
. The greater the AUC, the better the

model
is at predicting responses. For this example, using a lambda value of 3 resulted in the highest AUC, suggesting it is the best model for predicting future samples. Note the black, horizontal line near the bottom of the graph. This line represents the AUC if there is no any

predictive model
, and predicts every

observation
to be in the majority class. Any model whose AUC statistics approach or fall below this reference value is likely to be unreliable.

The plot displays results from JMPs
Fit Y by X
platform, which in this case performs a one-way analysis of

variance
along with

mean
comparisons.

Note
: the assumptions of independence behind the mean comparisons are not met because each of the models is fitted to the same data set. So you should interpret the results cautiously and use them primarily for relative comparisons. The same holds true for all plots of this type.

The output of this tab is similar to the
RMSE
tab, and contains the following additional element: