k层交叉检验（k-flod cross-validation）

最新推荐文章于 2025-06-07 14:15:32 发布

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最新推荐文章于 2025-06-07 14:15:32 发布

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本文链接：https://blog.youkuaiyun.com/dzyj211/article/details/52760168

本文详细介绍了K层交叉验证的概念及应用，这是一种常用的模型验证方法，通过将原始数据集随机分成K个子集，并依次使用每个子集作为验证集进行模型测试，剩余的数据则用作训练集。该方法确保了每个观测值都能被用于训练和验证，且只被用作一次验证。

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k层交叉检验（k-flod cross-validation）

当然这个称呼也是随着k值的变化而变化的，如果5-flod cross-validation就叫做5层交叉检验。

就是说把原始数据分成k份，选一份做为测试数据，剩下的k-1份做为训练数据。

下面附英文解释（To foreigner friends）

In K-foldcross-validation, the original sample is randomly partitioned into K subsamples.Of the Ksubsamples, a single subsample is retained as the validation data for testing the model, and the remaining (K − 1)subsamples are used as training data. The cross-validation process is thenrepeated Ktimes (the folds), with each of the K subsamplesused exactly once as the validation data. The K results fromthe folds then can be averaged (or otherwise combined) to produce a singleestimation. The advantage of this method over repeated random sub-sampling isthat all observations are used for both training and validation, and eachobservation is used for validation exactly once. 10-fold cross-validation iscommonly used.