逻辑回归系数

Question

我需要有关Logistic回归的帮助。 以下是我的数据：

ID        | Mach_1  | Mach_2 | Mach_3  | Mach_4 | Mach_5 | ..Mach300 | Rejected Unit (%) | Yield(%)
127189.11     1         0        1         1        1          0            0.23             98.0%
178390.11     0         0        0         1        0          0            0.10             90.0%
902817.11     1         0        1         0        1          0            0.60             94.0%
DSK1201.11    1         0        0         0        1          0            0.02             99.98%

我大约有300马赫列数和2K行。我想预测每台机器中有多少百分比对被拒收的设备做出了贡献。我想知道哪台机器是被拒绝的单元。

我已经完成了一些编码，但是遇到一些我不理解的错误以及如何解决它。 下面是我的代码：

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split

df = pd.read_csv('Data.csv')

#Convert ID into numerical
le = LabelEncoder()
labelencoder.fit_transform(df[:,0])

#Separate target variable and other columns
X = df.drop('Rejected Unit (%)',1)
y = df['Rejected Unit (%)']

#Split data into training and testing sets
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)
#Get the coefficient for each features column
import statsmodels.api as sm
model = sm.Logit(y_train,X_train)
res = mod.fit()
print(res.summary())

起初这是我的代码，然后出现错误。

ValueError: endog must be in the unit interval

然后我缩放y（目标变量），然后又收到另一个错误，我不知道为什么以及如何解决。

这是缩放数据后我最新的代码：

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split

df = pd.read_csv('Data.csv')

#Convert ID into numerical
le = LabelEncoder()
labelencoder.fit_transform(df[:,0])

#Separate target variable and other columns
X = df.drop('Rejected Unit (%)',1)
y = df['Rejected Unit (%)']

#scale target variable
from sklearn.preprocessing import MinMaxScaler
y_reshape = y.values.reshape(-1,1)
scaler = MinMaxScaler()
scaler.fit(y_reshape)
#change the numpy array of y_scale into dataframe
y = pd.DataFrame(y_scale)


#Split data into training and testing sets
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)
#Get the coefficient for each features column
import statsmodels.api as sm
model = sm.Logit(y_train,X_train)
res = mod.fit()
print(res.summary())

然后我收到错误消息：

有人可以帮助我吗？