Knowledge of the range, behavior, and feeding habits of large carnivores is fundamental to their successful conservation. Traditionally, the best method to obtain feeding data is through continuous observation, which is not always feasible. Reliable automated methods are needed to obtain sample sizes sufficient for statistical inference. Identification of large carnivore kill sites using Global Positioning System (GPS) data is gaining popularity. We assessed performance of generalized linear regression models (GLM) versus classification trees (CT) in a multipredator, multiprey African savanna ecosystem. We applied GLMs and CTs to various combinations of distance-traveled data, cluster durations, and environmental factors to predict occurrence of 234 female African lion (Panthera leo) kill sites from 1,477 investigated GPS clusters. Ratio of distance moved 24 hours before versus 24 hours after a cluster was the most important predictor variable in both GLM and CT analysis. In all cases, GLMs outperformed our cost-complexity-pruned CTs in their discriminative ability to separate kill from nonkill sites. Generalized linear models provided a good framework for kill-site identification that incorporates a hierarchal ordering of cluster investigation and measures to assess trade-offs between classification accuracy and time constraints. Implementation of GLMs within an adaptive sampling framework can considerably increase efficiency of locating kill sites, providing a cost-effective method for increasing sample sizes of kill data.