1、✌ 原理 用于将样本集合随机“打散”后划分为训练集、测试集(可理解为验证集,下同) 类似于交叉验证 2、✌ 函数形式 ShuffleSplit(n_splits=10, test_size=’default’, train_size=None, random_state=None) 3、✌ 重要参数 n_splits: 划分数据集的份数,类似于KFlod的折数,默认为10份 test_size...
n_splits:int, default=10 重复这个随机排列、分割的过程的次数,默认10次 import numpy as np from sklearn.model_selection import ShuffleSplit X=np.random.randint(1,100,20).reshape(10,2) X rs = ShuffleSplit(n_splits=10, test_size=0.25) rs for train , test in rs.split(X): print(f'trai...
X = np.arange(10) ss=ShuffleSplit(n_splits=5,test_size=0.25) n_fold=1 for train_indices,test_indices in ss.split(X): print('fold {}/5...'.format(n_fold)) print("train_indices",train_indices) print("test_indices:",test_indices) n_fold+=1 下面是交叉验证行为的可视化: ShuffleSpl...
# 需要导入模块: from sklearn.model_selection import ShuffleSplit [as 别名]# 或者: from sklearn.model_selection.ShuffleSplit importget_n_splits[as 别名]fromsklearn.model_selectionimportShuffleSplitimportnumpyasnp X = np.array([[1,2], [3,4], [5,6], [7,8]]) y = np.array([1,2,1,...
>>> import numpy as np >>> from sklearn.model_selection import ShuffleSplit >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [3, 4], [5, 6]]) >>> y = np.array([1, 2, 1, 2, 1, 2]) >>> rs = ShuffleSplit(n_splits=5, test_size=.25, random_state=...
1、✌ 原理 用于将样本集合随机“打散”后划分为训练集、测试集(可理解为验证集,下同) 类似于交叉验证 2、✌ 函数形式 ShuffleSplit(n_splits=10, test_size=’default’, train_size=None, random_state=None) 3、✌ 重要参数 n_splits:
TRAIN: [1 2 4 0] TEST: [3 5] TRAIN: [3 4 1 0] TEST: [5 2] TRAIN: [3 5 1 0] TEST: [2 4] >>> rs = ShuffleSplit(n_splits=5, train_size=0.5, test_size=.25, random_state=0) >>> for train_index, test_index in rs.split(X): ...
ShuffleSplit(n_splits=5, random_state=0, test_size=0.25, train_size=None) >>> for train_index, test_index in rs.split(X): ... print("TRAIN:", train_index, "TEST:", test_index) TRAIN: [1 3 0 4] TEST: [5 2] TRAIN: [4 0 2 5] TEST: [1 3] ...
通过n_splits产生指定数量的独立的【train/test】数据集,划分数据集划分成n组(n组索引值),其创建的每一组划分将保证每组类比的比例相同。比如第一组训练数据类别比例为2:1,则后面每组类别都满足这个比例。 ShuffleSplit()函数 cv_split = ShuffleSplit(n_splits=6, train_size=0.7, test_size=0.2) class ...
gss=GroupShuffleSplit(n_splits=1,train_size=.8,group_by="size")train,test=next(gss.split(X,y,groups_unbalanced)) I did a benchmark to analyze whether this feature is really helping. I used 13 data sets (mainly fromMoleculeNet) and clustered each of the 316 tasks with 4 clustering algo...