逻辑回归只能解决二分类问题
解决方法：OvR（One vs Rest）， OvO（One vs One）

N个类型就进行N次分类，选择得分最高的
对于逻辑回归，这里的分类是指分类的概率

N个类别就进行C(N,2)次分类，选择赢数最多的分类

OvO算法耗时更多，但分类更准确。

使用LogisticRegression提供的ovr和ovo

加载数据

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

OvR

from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression(multi_class='ovr')
log_reg.fit(X_train, y_train)
log_reg.score(X_test, y_test)  # 0.6578947368421053
plot_decision_boundary(log_reg, axis=[4,8,1.5,4.5])   # 见9-5
plt.scatter(iris.data[iris.target==0,0],iris.data[iris.target==0,1], color='red')
plt.scatter(iris.data[iris.target==1,0],iris.data[iris.target==1,1], color='blue')
plt.scatter(iris.data[iris.target==2,0],iris.data[iris.target==2,1], color='green')
plt.show()

OvO

from sklearn.linear_model import LogisticRegression
log_reg2 = LogisticRegression(multi_class='multinomial', solver='newton-cg')
log_reg2.fit(X_train, y_train)
log_reg2.score(X_test, y_test)   # 0.7894736842105263
plot_decision_boundary(log_reg2, axis=[4,8,1.5,4.5])
plt.scatter(iris.data[iris.target==0,0],iris.data[iris.target==0,1], color='red')
plt.scatter(iris.data[iris.target==1,0],iris.data[iris.target==1,1], color='blue')
plt.scatter(iris.data[iris.target==2,0],iris.data[iris.target==2,1], color='green')
plt.show()

使用所有数据

X = iris.data
y = iris.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression(multi_class='ovr')
log_reg.fit(X_train, y_train)
log_reg.score(X_test, y_test)    # 0.9473684210526315

log_reg2 = LogisticRegression(multi_class='multinomial', solver='newton-cg')
log_reg2.fit(X_train, y_train)
log_reg2.score(X_test, y_test)   # 1.0

使用scikit-learn中的OvR和OvO，能把所有二分类算法转换成多分类算法

log_reg = LogisticRegression()

from sklearn.multiclass import OneVsRestClassifier
ovr = OneVsRestClassifier(log_reg)
ovr.fit(X_train, y_train)
ovr.score(X_test, y_test)   # 0.9473684210526315

from sklearn.multiclass import OneVsOneClassifier
log_reg = LogisticRegression()
ovo = OneVsOneClassifier(log_reg)
ovo.fit(X_train, y_train)
ovo.score(X_test, y_test)   # 1.0