The One-Line Summary: Two extra lines inside the objective — report the score as the model grows, then ask whether to stop — let Optuna abandon 12 of 20 trials before they finished, cutting wall clock from 13.0s to 9.4s at a cost of 0.0005 log loss; the saving is smaller than the kill rate because a pruned trial still runs its warm-up, which is the detail nobody mentions.
The Two Lines That Do The Work
Every grid search has the same defect: it runs every candidate to completion, including the ones that were obviously hopeless after 20% of the work. A human watching a training curve would have killed them. Pruning is that human, automated.
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trial 7, learning_rate=0.011, depth=2
