Model Validation-Set Summaries(Available when you select the Multiple Fits over Number of Terms option in Bootstrap Forest Specification window.) Provides fit statistics for all the models fit. See Figure 6.10 and Multiple Fits Panel.
Specifications
Overall Statistics
Categorical ResponseA measure that can be applied to general regression models. It is based on the likelihood function L and is scaled to have a maximum value of 1. The value is 1 for a perfect model, and 0 for a model no better than a constant model. The Generalized RSquare measure simplifies to the traditional RSquare for continuous normal responses in the standard least squares setting. Generalized RSquare is also known as the Nagelkerke or Craig and Uhler R2, which is a normalized version of Cox and Snell’s pseudo R2.
The average of negative log(p), where p is the fitted probability associated with the event that occurred.
The root mean square error, adjusted for degrees of freedom. The differences are between 1 and p, the fitted probability for the response level that actually occurred.
The average of the absolute values of the differences between the response and the predicted response. The differences are between 1 and p, the fitted probability for the response level that actually occurred.
(Available only for categorical responses and if the response has a Profit Matrix column property or if you specify costs using the Specify Profit Matrix option.) Gives Decision Count and Decision Rate matrices for the training set, and for the validation and test sets if they are specified. See Additional Examples of Partitioning in Partition Models.
Continuous ResponseGives RMSE values, which are averaged over all trees, for In Bag and Out of Bag observations. Training set observations that are used to construct a tree are called in-bag observations. Training observations that are not used to construct a tree are called out-of-bag (OOB) observations.