Abstract

Ensemble Selection from Libraries of Models
Rich Caruana - Cornell University Alexandru Niculescu-Mizil - Cornell University Geoff Crew - Cornell University Alex Ksikes - Cornell University
We present a method for constructing ensembles from libraries of thousands of models. Model libraries are generated using different learning algorithms and parameter settings. Forward stepwise selection is used to add to the ensemble the models that maximize its performance. Ensemble selection allows ensembles to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area. Experiments with seven test problems and ten metrics demonstrate the benefit of ensemble selection.

Ensemble Selection from Libraries of Models

Rich Caruana - Cornell University
Alexandru Niculescu-Mizil - Cornell University
Geoff Crew - Cornell University
Alex Ksikes - Cornell University

We present a method for constructing ensembles from libraries of thousands of models. Model libraries are generated using different learning algorithms and parameter settings. Forward stepwise selection is used to add to the ensemble the models that maximize its performance. Ensemble selection allows ensembles to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area. Experiments with seven test problems and ten metrics demonstrate the benefit of ensemble selection.