HIGHLIGHTS
SUMMARY
This also explains why the most data intensive classification approaches, such as machine_learning, investigate relative short-term behaviors (resting, feeding, flying) using high frequency accelerometry data. The authors assessed multiple machine_learning classification frameworks and evaluated performance of models trained using combinations of 3 feature sets: target date GPS information only, arrangement of target date locations with locations during previous time periods, and remotely sensed habitat characteristics at GPS locations. Commercial software and modelling packages for opensource programming languages have improved access to machine_learning methods to non-experts, however much of the knowledge required for . . .
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