Large imbalanced tabular datasets make repeated gradient-boosted tree training expensive.
Existing coreset methods often lose accuracy when most majority examples are removed.
We present CRISP (Coreset Reduction via Importance-Stratified Pruning), a linear-time method that allocates a negative-class budget across quantile strata of a proxy-model score.
Sample weights account for unequal inclusion probabilities.
At 95% negative-class reduction on a production fraud dataset, CRISP trains on approximately 1.70M of 25M rows and retains 99.7% of full-data Average Precision.
This is a 93.2% reduction in total training rows.
On public CriteoPrivateAds, CRISP has the highest mean Average Precision at each tested rate from 90% to 99.4% majority reduction.
Sparkov results are mixed at lower rates, but CRISP has the highest mean at 99.2% and 99.4%.
Ablations identify budget allocation and inverse-propensity weighting as the main sources of the production-dataset gain.