提高随机森林的采样率能提升分类准确率，且最优值由数据特性决定。
Bootstrap Sampling Rate Greater than 1.0 May Improve Random Forest Performance
- 尝试大于1.0的采样率（如1.2至5.0），突破传统做法
- 在36个数据集上，更高采样率显著提升分类精度
- 最优采样率取决于数据特征，与模型超参无关
随机森林（RF）通常通过有放回抽样生成每棵决策树的训练集，样本量一般等于原始训练集大小 $N$。以往研究认为采样少于 $N$ 也能获得良好效果，而采样多于 $N$（即采样率 BR > 1.0）研究较少，常被视为无效。本文在36个不同数据集上重新评估了采样率从1.2到5.0的设置，发现较高采样率可带来统计上显著的分类准确率提升，优于标准设置（BR ≤ 1.0）。我们分析了采样率对决策树叶节点结构的影响，并探究影响最优采样率的因素。结果表明，最优采样率主要由数据集特性决定，而非随机森林的超参数。
原文摘要 · Abstract (English)
Random forests (RFs) utilize bootstrap sampling to generate individual training sets for each component tree by sampling with replacement, with the sample size typically equal to that of the original training set ($N$). Previous research indicates that drawing fewer than $N$ observations can also yield satisfactory results. The ratio of the number of observations in each bootstrap sample to the total number of training instances is referred to as the bootstrap rate (BR). Sampling more than $N$ observations (BR $>$ 1.0) has been explored only to a limited extent and has generally been considered ineffective. In this paper, we revisit this setup using 36 diverse datasets, evaluating BR values ranging from 1.2 to 5.0. Contrary to previous findings, we show that higher BR values can lead to statistically significant improvements in classification accuracy compared to standard settings (BR $\leq$ 1.0). Furthermore, we analyze how BR affects the leaf structure of decision trees within the RF and investigate factors influencing the optimal BR. Our results indicate that the optimal BR is primarily determined by the characteristics of the data set rather than the RF hyperparameters.
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