sklearn.model_selection.StratifiedKFold

class sklearn.model_selection.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None)

StratifiedKFold是k-fold的变种，会返回Stratified（分层）的折叠；每个小集合中，各个类别的样例比例大致和完整数据集中相同。

参数：
n_splits:int, default=5 表示把数据划分成几等分
shuffle:bool, default=False 在每次划分时，是否进行洗牌

当shuffle为True，random_state会影响索引的顺序，每次划分的结果会不一样
random_stateint or RandomState instance, default=None :随机种子数`

>>> from sklearn.model_selection import StratifiedKFold>>> X = np.ones(10)
>>> y = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
>>> skf = StratifiedKFold(n_splits=3)
>>> for train, test in skf.split(X, y):
...     print("%s  %s" % (train, test))
[2 3 6 7 8 9] [0 1 4 5]
[0 1 3 4 5 8 9] [2 6 7]
[0 1 2 4 5 6 7] [3 8 9]

参考：
sklearn中文文档：http://scikitlearn.com.cn/0.21.3/30/#31221-k
StratifiedKFold和KFold生成交叉验证数据集的区别:https://blog.csdn.net/MsSpark/article/details/84455402

sklearn.model_selection.StratifiedKFold相关推荐

sklearn.model_selection的StratifiedKFold实例
from sklearn.model_selection import StratifiedKFold X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [5 ...
【模型评估与选择】sklearn.model_selection.KFold
1. 描述 KFold divides all the samples in k groups of samples, called folds (if k = n, this is equivale ...
sklearn中sklearn.model_selection的应用与实践
强大的sklearn库可以解决的问题: train_test_split返回切分的数据集train/test: train_test_split(*array,test_size=0.25,train ...
sklearn.model_selection.train_test_split随机划分训练集和测试集
1 函数用途 train_test_split()是交叉验证中常用的函数,功能是将数组或矩阵按比例随机划分为训练集和测试集,使用方法为: X_train,X_test, y_train, y_test ...
【模型评估与选择】sklearn.model_selection.train_test_split
1. 描述 Split arrays or matrices into random train and test subsets 2. 语法 train_test_split(*arrays, ** ...
数据挖掘原理与算法：机器学习-＞{[sklearn. model_selection. train_test_split]、[h2o]、[网格搜索]、[numpy]、[plotly.express]}
数据挖掘原理与算法:机器学习->{[sklearn. model_selection. train_test_split].[h2o].[网格搜索].[numpy].[plotly.expres ...
TensorFlow 人脸识别实验 ImportError: No module named 'sklearn.model_selection'
今天在运行TensorFlow 人脸识别 python项目时,报 ImportError: No module named 'sklearn.model_selection',当我使用pip inst ...
python sklearn.model_selection.ShuffleSplit()函数的用法
sklearn.model_selection.ShuffleSplit类用于将样本集合随机"打散"后划分为训练集.测试集
11_模型的选择与调优,交叉验证,超参数搜索-网格搜索sklearn.model_selection.GridSearchCV
1.交叉验证交叉验证:为了让被评估的模型更加准确可信. 交叉验证:将拿到的数据,分为训练和验证集.以下图为例:将数据分成5份,其中一份作为验证集.然后经过5次(组)的测试,每次都更换不同的验证集.即 ...

sklearn.model_selection.StratifiedKFold

sklearn.model_selection.StratifiedKFold

StratifiedKFold是k-fold的变种，会返回Stratified（分层）的折叠；每个小集合中，各个类别的样例比例大致和完整数据集中相同。

sklearn.model_selection.StratifiedKFold相关推荐

最新文章

热门文章