The reports identify
significant terms in the model and generate common business graphics,
such as lift charts. The results include statistics for training and
validation data. The SAS Rapid Predictive Modeler process divides
the input data into training data and validation data. Training data
is used to compute the parameters for each model, resulting in the
training fit statistics. Validation data is then scored with each
model, resulting in the validation fit statistics. The validation
fit statistics are used to compare models and detect overfitting.
If the training statistics are significantly better than the validation
statistics, then you would suspect overfitting, which occurs when
the model is trained to detect random signals in the data. Models
with the best validation statistics are generally preferred.
The SAS Rapid Predictive
Modeler automatically generates a concise set of core reports that
provide a summary of the data source and variables that were used
for modeling, a ranking of the important predictor variables, multiple
fit statistics that evaluate the accuracy of the model, and a model
scorecard.