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Lecture Notes in Computer Science
Gradient-boosted regression trees (GBRTs) have proven to be an effective solution to the learning-to-rank problem. This work proposes and evaluates techniques for training GBRTs that have efficient runtime characteristics. Our approach is based on the simple idea that compact, shallow, and balanced trees yield faster predictions: thus, it makes sense to incorporate some notion of execution cost during training to "encourage" trees with these topological characteristics. We propose twodoi:10.1007/978-3-642-36973-5_13 fatcat:ffyx22fkzjasrdibey25j6bxee