我做了一个逻辑回归，并添加了StandardScalar（）-用于归一化模型。我得到了系数，和原来的系数有什么不同。（这是一个我看到结果WO Standardscalar（）的例子）

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

# Create LogisticRegression model
log_model = make_pipeline(StandardScaler(), LogisticRegression())

# Fit our data
log_model.fit(X,Y)

# Check our accuracy
print(log_model.score(X,Y))
# Check percentage of women that had affairs
print(Y.mean())

# Use zip to bring the column names and the np.transpose function to bring together the coefficients from the model
coeff_df = pd.DataFrame(zip(X.columns, np.transpose(log_model.steps[1][1].coef_)))

我想得到原始的系数：我尝试了以下操作：

print(log_model.steps[0][1].inverse_transform(log_model.steps[1][1].coef_, copy=None))

我试过逆变换，但完全错误。你能帮我吗，先谢了