代碼：

 1 # -*- coding: utf-8 -*-
 2 """
 3 Created on Tue Jul 17 10:13:20 2018
 4 
 5 @author: zhen
 6 """
 7 
 8 from sklearn.linear_model import LogisticRegression
 9 from sklearn.svm import LinearSVC
10 import mglearn
11 import matplotlib.pyplot as plt
12 
13 x, y = mglearn.datasets.make_forge()
14
 
 
15 fig, axes = plt.subplots(1, 2, figsize=(10,3))
16 # 線性支持向量機與邏輯回歸進行比較
17 for model, ax in zip([LinearSVC(), LogisticRegression()], axes):
18     clf = model.fit(x, y)
19     mglearn.plots.plot_2d_separator(clf, x, fill=False, eps=0.5, ax=ax, alpha=0.7)
20     mglearn.discrete_scatter(x[:, 0], x[:, 1], y, ax=ax)
 
21     ax.set_title("{}".format(clf.__class__.__name__))
22     ax.set_xlabel("Feature 0")
23     ax.set_ylabel("Feature 1")
24 axes[0].legend()
25 
26 # 
27 from sklearn.datasets import load_breast_cancer
28 from sklearn.model_selection import train_test_split
29 cancer = load_breast_cancer()
30 
31 x_train, x_test, y_train, y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=42)
 
32 # 使用默認配置參數 
33 log_reg = LogisticRegression().fit(x_train, y_train)
34 
35 print("="*25+"邏輯回歸（C=1）"+"="*25)
36 print("Training set score：{:.3f}".format(log_reg.score(x_train, y_train)))
37 print("Test set score：{:.3f}".format(log_reg.score(x_test, y_test)))
38 
39 # 使用配置參數C=100
40 log_reg_100 = LogisticRegression(C=100).fit(x_train, y_train)
41 
42 print("="*25+"邏輯回歸（C=100）"+"="*25)
43 print("Training set score：{:.3f}".format(log_reg_100.score(x_train, y_train)))
44 print("Test set score：{:.3f}".format(log_reg_100.score(x_test, y_test)))
45 
46 # 使用配置參數C=0.01
47 log_reg_001 = LogisticRegression(C=0.01).fit(x_train, y_train)
48 
49 print("="*25+"邏輯回歸（C=0.01）"+"="*25)
50 print("Training set score：{:.3f}".format(log_reg_001.score(x_train, y_train)))
51 print("Test set score：{:.3f}".format(log_reg_001.score(x_test, y_test)))
52 print("="*25+"邏輯回歸&線性支持向量機"+"="*25)
53 # 可視化
54 fig, axes = plt.subplots(1, 1, figsize=(10,3))
55 plt.plot(log_reg.coef_.T, ‘o‘, label="C=1")
56 plt.plot(log_reg_100.coef_.T, ‘^‘, label="C=100")
57 plt.plot(log_reg_001.coef_.T, ‘v‘, label="C=0.01")
58 plt.xticks(range(cancer.data.shape[1]), cancer.feature_names, rotation=90)
59 plt.hlines(0, 0, cancer.data.shape[1])
60 
61 plt.ylim(-5, 5)
62 
63 plt.xlabel("Cofficient indes")
64 plt.ylabel("Cofficient magnitude")
65 
66 plt.legend()

結果：

邏輯回歸&線性支持向量機