Question

我想将名为（my_data2）的数据表划分为两个名为（学习样本和测试样本）的样本。如何在我的表的第一部分（第一个样本）上应用逻辑回归，然后在第二部分应用预测？谢谢。这是我的编码;

import numpy as np
from statsmodels.formula.api import logit

FNAME2 ="C:/Users/lenovo/Desktop/Nouveau dossier (2)/table.csv"
FinalTableau=np.savetxt(FNAME2,my_data[index_to_use] , delimiter=",")
my_data2 = np.genfromtxt (FNAME2, delimiter = ',')

x= my_data2 [:,1]
a= my_data2[:,3]
#x with values 1 and 2
print x

#converts my binary data series from (1, 2) to (0,1)
x= my_data[:, 1] - 1
print x

form = 'x ~ a'
affair_model = logit (form, my_data2)

affair_result = affair_model.fit ()

print affair_result.summary ()
print affair_result.predict()

Answer 1

将my_data2分成两个大小相等的数组：

N = len(my_data2)//2
learning_sample, test_sample = my_data2[:N], my_data2[N:]

例如，

import numpy as np
from statsmodels.formula.api import logit

FNAME2 = "C:/Users/lenovo/Desktop/Nouveau dossier (2)/table.csv"
FinalTableau = np.savetxt(FNAME2, my_data[index_to_use], delimiter=",")
my_data2 = np.genfromtxt(FNAME2, delimiter=',')

# converts my binary data series from (1, 2) to (0,1)
my_data2[:, 1] -= 1  
# print my_data2

N = len(my_data2)//2
learning_sample, test_sample = my_data2[:N], my_data2[N:]
x = learning_sample[:, 1]
a = learning_sample[:, 3]
# x with values 1 and 2
print x

form = 'x ~ a'
affair_model = logit(form, learning_sample)
affair_result = affair_model.fit()

print affair_result.summary()
print affair_result.predict()

如何在两个样本中划分名为（my_data2）的数据表

1 个答案: