Python 使用交叉验证确定的模型验证外部数据
我正在用Python学习数据挖掘。我正在尝试交叉验证Python 使用交叉验证确定的模型验证外部数据,python,cross-validation,Python,Cross Validation,我正在用Python学习数据挖掘。我正在尝试交叉验证 import numpy as np from sklearn.cross_validation import KFold X = np.array([0.1, 0.2, 0.3, 0.4]) Y = np.array([False,True,True,False]) kf=KFold(4,n_folds=2) for train_index, test_index in kf: X_train, X_test = X[train_in
import numpy as np
from sklearn.cross_validation import KFold
X = np.array([0.1, 0.2, 0.3, 0.4])
Y = np.array([False,True,True,False])
kf=KFold(4,n_folds=2)
for train_index, test_index in kf:
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
现在我有了一个新的列表<代码>X=[0.25,0.33,0.21,0.101];Y=[True,False,False,True]如何根据使用上述代码确定的模型验证结果?功能与模型确定无关 它只是将数据和标签拆分为折叠 如果添加到循环中:
print(X_train, X_test)
print(Y_train, Y_test)
您可以在每次迭代中看到折叠:
# Iteration 1
# Train Test
[ 0.3 0.4] [ 0.1 0.2]
[ True False] [False True]
# Iteration 2
# Train Test
[ 0.1 0.2] [ 0.3 0.4]
[False True] [ True False]
不清楚您要验证什么以及如何验证。请阅读精细手册: