论文标题
随机森林的概括误差的置信区间
Confidence Intervals for the Generalisation Error of Random Forests
论文作者
论文摘要
在基于整体的学习模型（例如随机森林）中，脱落外误差通常用作概括误差的估计。我们使用Delta-Method-bootstrap和Jackknife-bootstrap技术提出了此数量的置信区间。这些方法不需要种植任何其他树木。我们表明，在实际和模拟的示例中，这些新的置信区间比幼稚的置信区间提高了覆盖范围。
Out-of-bag error is commonly used as an estimate of generalisation error in ensemble-based learning models such as random forests. We present confidence intervals for this quantity using the delta-method-after-bootstrap and the jackknife-after-bootstrap techniques. These methods do not require growing any additional trees. We show that these new confidence intervals have improved coverage properties over the naive confidence interval, in real and simulated examples.
