python风控评分卡建模和风控常识(博客主亲自录制视频教程)
原文链接
https://blog.csdn.net/wqh_jingsong/article/details/77896449
StratifiedKFold用法类似Kfold,但是他是分层采样,确保训练集,测试集中各类别样本的比例与原始数据集中相同。
例子:
import numpy as np
from sklearn.model_selection import KFold,StratifiedKFold
X=np.array([ [1,2,3,4], [11,12,13,14], [21,22,23,24], [31,32,33,34], [41,42,43,44], [51,52,53,54], [61,62,63,64], [71,72,73,74] ]) y=np.array([1,1,0,0,1,1,0,0]) #n_folds这个参数没有,引入的包不同, floder = KFold(n_splits=4,random_state=0,shuffle=False) sfolder = StratifiedKFold(n_splits=4,random_state=0,shuffle=False) for train, test in sfolder.split(X,y): print(‘Train: %s | test: %s‘ % (train, test)) print(" ") for train, test in floder.split(X,y): print(‘Train: %s | test: %s‘ % (train, test)) print(" ")
结果:
1.
Train: [1 3 4 5 6 7] | test: [0 2]
Train: [0 2 4 5 6 7] | test: [1 3]
Train: [0 1 2 3 5 7] | test: [4 6]
Train: [0 1 2 3 4 6] | test: [5 7]
2.
Train: [2 3 4 5 6 7] | test: [0 1]
Train: [0 1 4 5 6 7] | test: [2 3]
Train: [0 1 2 3 6 7] | test: [4 5]
Train: [0 1 2 3 4 5] | test: [6 7]
分析:可以看到StratifiedKFold 分层采样交叉切分,确保训练集,测试集中各类别样本的比例与原始数据集中相同。
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原文地址:https://www.cnblogs.com/webRobot/p/10438341.html
时间: 2024-11-05 22:54:28