document: Model compression Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers . Unfortunately , the space required to store this many classifiers , and the time required to execute them at run-time , prohibits their use in applications where test sets are large ( e.g. Google ) , where storage space is at a premium ( e.g. PDAs ) , and where computational power is limited ( e.g. hea-ring aids ) . We present a method for `` compressing '' large , complex ensembles into smaller , faster models , usually without significant loss in performance .
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