Parameters | Predictive Modeling | Inner Loop Algorithm

Inner Loop Algorithm
Use this feature to specify the method to evaluate the learning curves within each inner loop.
The learning curve algorithm operates on successively larger portions of the original data. Full data is the portion on which the current point of the learning curve is being fit. The remaining part of the original data is the test set .
When full data is chosen, one can optionally perform cross-validation by partitioning it several times into training and test sets. The former are the subsets and the latter are the hold-out sets .
Available methods for evaluating the learning curves are described in the table below:
To Specify the Method Used to Evaluate Learning Curves: