Unverified50% confidenceFactExact time
The study compared multiple classifiers, including K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Random Forest (RF).
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Long-term
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11/3/2026
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UnverifiedUnlike the parallel bagging of random forests, GB's serial additive structure makes it more sensitive to hard-to-fit samples, and it often achieves high accuracy on tabular data, but it trains slowly and is sensitive to hyperparameters (learning rate, tree depth, number of iterations), requiring cross-validation tuning to avoid overfitting.69% similarUnverified该测试采用KL散度（KLD）作为客观量化指标，衡量量化后输出分布与BF16参考分布的偏离程度，KLD越低说明量化造成的信息损失越小66% similarUnverifiedExact K-nearest neighbor (KNN) requires traversing all candidate vectors, with time complexity growing linearly with data volume, which is often unacceptable at the scale of hundreds of millions.66% similarUnverifiedThe choice of k=8 is a compromise between cost and statistical power: the larger k is, the narrower the confidence interval, but the number of generation calls also grows linearly.64% similarUnverified从KL散度角度看，每一代合成数据与真实分布之间的散度都在单调递增，且误差会在迭代中累积63% similar
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