- 我的数据集有
42000
行 - 我需要将数据集分为
训练、交叉验证和测试
集,分割比例为60%、20% 和20%
。这是根据 Andrew Ng 教授在他的 ml-class 讲座中的建议。 - 我意识到 scikit-learn 有一个方法 train_test_split去做这个。但是我无法让它工作,所以我在一个线性命令中将拆分为
0.6, 0.2, 0.2
我做的是
# split data into training, cv and test sets
from sklearn import cross_validation
train, intermediate_set = cross_validation.train_test_split(input_set, train_size=0.6, test_size=0.4)
cv, test = cross_validation.train_test_split(intermediate_set, train_size=0.5, test_size=0.5)
# preparing the training dataset
print 'training shape(Tuple of array dimensions) = ', train.shape
print 'training dimension(Number of array dimensions) = ', train.ndim
print 'cv shape(Tuple of array dimensions) = ', cv.shape
print 'cv dimension(Number of array dimensions) = ', cv.ndim
print 'test shape(Tuple of array dimensions) = ', test.shape
print 'test dimension(Number of array dimensions) = ', test.ndim
然后得到结果
training shape(Tuple of array dimensions) = (25200, 785)
training dimension(Number of array dimensions) = 2
cv shape(Tuple of array dimensions) = (8400, 785)
cv dimension(Number of array dimensions) = 2
test shape(Tuple of array dimensions) = (8400, 785)
test dimension(Number of array dimensions) = 2
features shape = (25200, 784)
labels shape = (25200,)
如何在一个命令中完成这项工作?
最佳答案
阅读train_test_split的源代码及其同伴类 ShuffleSplit并使其适应您的用例。这不是一个很大的功能,应该不会很复杂。
关于python - 使用 train_test_split 的一个命令创建数据集的多分割,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/13346318/