Prediction Rule Ensembles
Compute bootstrapped null interaction prediction rule ensembles
Model set up for train function of package caret
Coefficients for a General Prediction Ensemble (gpe)
Coefficients for the final prediction rule ensemble
Plot correlations between baselearners in a prediction rule ensemble (...
Full k-fold cross validation of a prediction rule ensemble (pre)
Explain predictions from final prediction rule ensemble
Derive a General Prediction Ensemble (gpe)
Default penalized trainer for gpe
Get rule learner for gpe which mimics behavior of pre
Sampling Function Generator for gpe
Learner Functions Generators for gpe
Calculate importances of baselearners and input variables in a predict...
Calculate interaction statistics for variables in a prediction rule en...
Sampling function generator for specifying varying maximum tree depth ...
Compute the average dataset over imputed datasets.
Fit a prediction rule ensemble to multiply-imputed data (experimental)
Create partial dependence plot for a pair of predictor variables in a ...
Plot method for class pre
Derive a prediction rule ensemble
Predicted values based on gpe ensemble
Predicted values based on final prediction rule ensemble
Print a General Prediction Ensemble (gpe)
Print method for objects of class pre
Get the optimal lambda and gamma parameter values for an ensemble of g...
Dealing with rare factor levels in fitting prediction rule ensembles.
Wrapper Functions for terms in gpe
Create partial dependence plot for a single variable in a prediction r...
Summary method for a General Prediction Ensemble (gpe)
Summary method for objects of class pre
Derives prediction rule ensembles (PREs). Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables.