Stochastic Optimization Forests
- URL: http://arxiv.org/abs/2008.07473v6
- Date: Wed, 16 Mar 2022 06:26:45 GMT
- Title: Stochastic Optimization Forests
- Authors: Nathan Kallus and Xiaojie Mao
- Abstract summary: We show how to train forest decision policies by growing trees that choose splits to directly optimize the downstream decision quality, rather than splitting to improve prediction accuracy as in the standard random forest algorithm.
We show that our approximate splitting criteria can reduce running time hundredfold, while achieving performance close to forest algorithms that exactly re-optimize for every candidate split.
- Score: 60.523606291705214
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study contextual stochastic optimization problems, where we leverage rich
auxiliary observations (e.g., product characteristics) to improve decision
making with uncertain variables (e.g., demand). We show how to train forest
decision policies for this problem by growing trees that choose splits to
directly optimize the downstream decision quality, rather than splitting to
improve prediction accuracy as in the standard random forest algorithm. We
realize this seemingly computationally intractable problem by developing
approximate splitting criteria that utilize optimization perturbation analysis
to eschew burdensome re-optimization for every candidate split, so that our
method scales to large-scale problems. We prove that our splitting criteria
consistently approximate the true risk and that our method achieves asymptotic
optimality. We extensively validate our method empirically, demonstrating the
value of optimization-aware construction of forests and the success of our
efficient approximations. We show that our approximate splitting criteria can
reduce running time hundredfold, while achieving performance close to forest
algorithms that exactly re-optimize for every candidate split.
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