如何将数据集拆分/分区为训练和测试数据集,例如交叉验证？

import numpy
# x is your dataset
x = numpy.random.rand(100,5)
numpy.random.shuffle(x)
training,test = x[:80,:],x[80:,:]

要么

import numpy
# x is your dataset
x = numpy.random.rand(100,5)
indices = numpy.random.permutation(x.shape[0])
training_idx,test_idx = indices[:80],indices[80:]
training,test = x[training_idx,x[test_idx,:]

repeatedly partition the same data set for cross validation有很多种方法.一种策略是从数据集重新采样,重复：

import numpy
# x is your dataset
x = numpy.random.rand(100,5)
training_idx = numpy.random.randint(x.shape[0],size=80)
test_idx = numpy.random.randint(x.shape[0],size=20)
training,:]

最后,sklearn包含several cross validation methods(k-fold,leave-n-out,…).它还包括更先进的“stratified sampling”方法,这些方法创建了与某些功能相关的数据分区,例如,以确保在训练和测试集中有相同比例的正面和负面示例.