print.logforest

 object of class <code>logforest</code>.


Prints a matrix of the most important predictors and interactions, their variable importance scores, and frequency of occurrence for objects fitted by logforest.<p></p><p></p>

Two classification ensemble methods based on logic regression models.  LogForest uses a bagging approach to construct an ensemble of logic regression models.  LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.

print.logforest: Prints Output for a Logic Forest Model

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