fromsklearn.model_selectionimporttrain_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2, stratify=y,# 按照标签来分层采样 shuffle=True,# 是否先打乱数据的顺序再划分 random_state=1)# 控制将样本随机打乱 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. ...
train_split_test只把原数据集按test_size随机不重复地划分成训练集和测试集,用训练集来验证模型,用测试集来评估模型的得分高低。由于只划分一次,所以没有交叉验证。 fromsklearn.model_selectionimporttrain_test_split x_train,x_test,y_train,y_test=train_test_split(train_data,train_lable,test_size=0.3,r...
importnumpyasnpfromsklearn.model_selectionimportKFold# X = range(10)X = np.array([1,3,5,7,9,21])# kf = KFold(n_splits=3, random_state=None, shuffle=True)kf = KFold(n_splits=3, random_state=24, shuffle=True)fortrain,testinkf.split(X):print('train_index:',train)print('te...
1、KFold >>> import numpy as np >>> from sklearn.model_selection import KFold >>> X = ["a", "b", "c", "d"] >>> kf = KFold(n_splits=2) >>> for train, test in kf.split(X): ... print("%s %s" % (train, test)) [2 3] [0 1] [0 1] [2 3] 1. 2. 3....
用法:sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) 参数: n_splits:int,默认= 10 re-shuffling和拆分迭代的次数。 test_size:float或int,默认为None 如果为float,则应在0.0到1.0之间,并且代表要包含在测试拆分中的数据集的比例。
sklearn.cross_validation.StratifiedShuffleSplit(y, n_iter=10, test_size=0.1, train_size=None, random_state=None) 新类的使用: sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, test_size=’default’, train_size=None, random_state=None)来源...
train_size : float or int, default=None. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test siz...
class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) 分层ShuffleSplit cross-validator 提供训练/测试索引以拆分训练/测试集中的数据。 这个交叉验证对象是 StratifiedKFold 和 ShuffleSplit 的合并,它返回分层随机折叠。通过保留每个类别的样本...
StratifiedKFold(n_splits=10, shuffle=True, random_state=0) 和 StratifiedShuffle拆分 StratifiedShuffleSplit(n_splits=10, test_size=’default’, train_size=None, random_state=0) 以及使用 StratifiedShuffleSplit 的优势是什么 原文由 gabboshow 发布,翻译遵循 CC BY-SA 4.0 许可协议 python...
class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, test_size=’default’, train_size=None, random_state=None) n_splits:整数,默认值为10。重新打乱分割的迭代次数 test_size:浮点数,None。分割后的测试集大小,默认为浮点数0.1(train_size没有被设置,否则为训练集大小的补集) 如果为浮点数...