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我们可以选择在整个人口中随机抽取一个 60 大小的样本,但在这些城镇中,随机样本可能不太平衡,因此会产生偏差,导致估计误差很大。
相反,如果我们选择从 A、B 和 C 镇分别抽取 10、20 和 30 个随机样本,那么我们可以在总样本大小相同的情况下,产生较小的估计误差。
使用 python 可以很容易地做到这一点:
水塘采样
假设您有一个项目流,它长度较大且未知以至于我们只能迭代一次。 创建一个算法,从这个流中随机选择一个项目,这样每个项目都有相同的可能被选中。
移除第一个项目的概率是项目 3 被选中的概率乘以项目 1 被随机选为水塘中 2 个要素的替代候选的概率。这个概率是: 2/3*1/2 = 1/3 因此,选择项目 1 的概率为: 1–1/3=2/3
在这个算法中,我们最终从 Tomek Links 中删除了大多数元素,这为分类器提供了一个更好的决策边界。
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