【模型评估与选择】sklearn.model_selection.KFold

最新推荐文章于 2025-05-15 14:04:53 发布

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最新推荐文章于 2025-05-15 14:04:53 发布 · 1.4k 阅读

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KFold是一种交叉验证方法，将数据集划分为k个互斥的子集，每次使用k-1个子集进行训练，剩余子集用于测试。参数包括n_splits（默认3）用于指定折数，shuffle（默认False）决定是否洗牌，以及random_state控制随机性。方法包括get_n_splits返回划分次数和split生成训练测试集的索引。实例展示了3折交叉验证的切分方式。

1. 描述
KFold divides all the samples in k groups of samples, called folds (if k = n, this is equivalent to the Leave One Out strategy), of equal sizes (if possible). The prediction function is learned using k - 1 folds, and the fold left out is used for test.

2. 语法
sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

3. 参数:
1. n_splits: int, default=3
Number of folds. Must be at least 2.
将训练/测试数据集划分n_splits个互斥子集，每次用n_splits-1个子集的并集作为训练集，余下的子集作为测试集
2. shuffle:boolean, optional
Whether to shuffle the data before splitting into batches.
shuffle= False:不洗牌，每次运行结果相同，相当于random_state=整数
shuffle=True:洗牌，每次运行结果不同
shuffle=True和random_state=整数: 每次运行结果相同
3. random_state: int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If Rand