sklearn FKold K折交叉验证 k-fold cross validation

C:\Users\pcl>conda activate torch38

(torch38) C:\Users\pcl>python
Python 3.8.11 (default, Aug  6 2021, 09:57:55) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from sklearn.model_selection import KFold
>>> import numpy as np
>>> import pandas as pd
>>> xt=np.random.randint(5,27,20)
>>> xt = xt.reshape(5,4)
>>> xt
array([[17, 13, 23, 26],
       [ 8, 10, 13,  8],
       [24,  7, 10, 23],
       [21, 13, 13, 21],
       [10,  9, 12, 10]])
>>> yt = np.random.randint(0,3,5)
>>> yt
array([1, 1, 1, 0, 2])
>>> folds = KFold(n_splits=5, shuffle = True, random_state=2021)
>>> xt = pd.DataFrame(xt)
>>> for tidx , vidx in folds.split(xt, yt):
...    vdf,vl = xt.iloc[vidx,:], yt[vidx]
...    tdf, tl = xt.iloc[tidx,:], yt[tidx]
...    print('fold id',tidx, vidx)
...
fold id [0 1 3 4] [2]
fold id [1 2 3 4] [0]
fold id [0 1 2 4] [3]
fold id [0 2 3 4] [1]
fold id [0 1 2 3] [4]
>>>

标签：FKold,tidx,cross,yt,fold,vidx,id,xt
来源： https://blog.csdn.net/CAIYUNFREEDOM/article/details/121648798