A warm-start plus early-stopping estimator gives variable importance close to full retraining for wide neural networks and gradient-boosted trees, with lower compute.
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Reliable and scalable variable importance estimation via warm-start and early stopping
A warm-start plus early-stopping estimator gives variable importance close to full retraining for wide neural networks and gradient-boosted trees, with lower compute.