Research map / Learning theory and algorithms / Prediction and decision losses
Learning Gradient Boosted Decision Trees with Algorithmic Recourse
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Proposes a training algorithm for gradient boosted decision trees that ensures algorithmic recourse actions exist for undesired predictions, rather than extracting recourse from an already-trained model.
Problem
Algorithmic recourse often fails to find valid, executable actions because such actions do not always exist for models optimized only for accuracy, such as gradient boosted decision trees.
Approach
Instead of extracting recourse actions from a fixed learned model, the method learns gradient boosted decision trees under a constraint that guarantees recourse actions exist for the undesired predictions.
Experimental setup
Builds on: gradient boosted decision trees
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