绘制逻辑回归非标度值

# ------ LOGISTIC REGRESSION ------ #

# --- Importing the Libraries --- #

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from matplotlib.colors import ListedColormap

# --- Importing the Dataset --- #

path = '/home/bohrz/Desktop/Programação/Machine Learning/Part 3 - ' \
       'Classification/Section 14 - Logistic Regression/Social_Network_Ads.csv'
dataset = pd.read_csv(path)
X = dataset.iloc[:, 2:4].values
y = dataset.iloc[:, -1].values

# --- Splitting the Dataset into Training and Test set --- #

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                    random_state=0)

# --- Feature Scaling --- #

sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

# --- Fitting the Logistic Regression Model to the Dataset --- #

classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)

# --- Predicting the Test set results --- #

y_pred = classifier.predict(X_test)

# --- Making the Confusion Matrix --- #

cm = confusion_matrix(y_test, y_pred)

# --- Visualizing Logistic Regression results --- #

# --- Visualizing the Training set results --- #

X_set_train, y_set_train = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start=X_set_train[:, 0].min(),
                               stop=X_set_train[:, 0].max(), step=0.01),
                     np.arange(start=X_set_train[:, 1].min(),
                               stop=X_set_train[:, 1].max(), step=0.01))

# Building the graph contour based on classification method
Z_train = np.array([X1.ravel(), X2.ravel()]).T
plt.contourf(X1, X2, classifier.predict(Z_train).reshape(X1.shape), alpha=0.75,
                                                         cmap=ListedColormap(
                                                             ('red', 'green')))

# Apply limits when outliers are present
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())

# Creating the scatter plot of the Training set results
for i, j in enumerate(np.unique(y_set_train)):
    plt.scatter(X_set_train[y_set_train == j, 0], X_set_train[y_set_train == j,
                                                              1],
                c=ListedColormap(('red', 'green'))(i), label=j)

plt.title('Logistic Regression (Trainning set results)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

我的问题是:如何在没有尺度的情况下绘制结果?我尝试在代码中的几个地方使用invert\u transform()方法,但没有任何帮助。

提前谢谢你。