（九）逻辑回归多分类应用
逻辑回归（Logistics Regression）属于分类算法，最适合解决二分类问题，也可以解决多分类问题，下面两个例子都是解决多分类的应用
一、鸢尾花案例
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressioniris = datasets.load_iris()
X = iris.data
y = iris.targetX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)1.1 OvR：（One vs Rest）一对剩余
将所有类别分为两类，某一类 和 非这一类（其它所有类别），对新样本对于这两类进行概率计算
例如：对要预测的新样本计算为A类的概率、非A类概率；计算为B类的概率、非B类的概率；
计算为C类的概率、非C类的概率......
将这个新样本分到得分最高的那一类，用二分类的思想实现了多分类
"""
可以调用sklearn中封装的OneVsRestClassifier类，调用任意二分类算法进行多分类
例如：
from sklearn.multiclass import OneVsRestClassifier
lgr1 = LogisticRegression()
ovr = OneVsRestClassifier(lgr1)
ovr.fit(X_train, y_train)
"""
lgr1 = LogisticRegression(multi_class='ovr', solver='liblinear')
lgr1.fit(X_train, y_train)
lgr1.score(X_test, y_test)
"""
训练的模型如下：
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='ovr', n_jobs=None, penalty='l2',
random_state=None, solver='liblinear', tol=0.0001, verbose=0,
warm_start=False)
"""
# 0.94736842105263151.2 OvO:（One vs One）一对一
对要预测的新样本进行两两类别求概率，最后投票决定
例如：对某样本求为A类别的概率，为B类别的概率；求为B类别的概率，为C类别的概率；
求为A类别的概率，为C类别的概率， 然后根据在哪个类别中数量最大进行投票决定它的类别
"""
可以调用sklearn中封装的OneVsOneClassifier类，调用任意二分类算法进行多分类
例如：
from sklearn.multiclass import OneVsOneClassifier
lgr2 = LogisticRegression()
ovo = OneVsOneClassifier(lgr2)
ovo.fit(X_train, y_train)
"""
lgr2 = LogisticRegression(multi_class='multinomial', solver='newton-cg')
lgr2.fit(X_train, y_train)
lgr2.score(X_test, y_test)
"""
训练的模型如下：
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='multinomial', n_jobs=None, penalty='l2',
random_state=None, solver='newton-cg', tol=0.0001, verbose=0,
warm_start=False)
"""
# 1.0总结：实际检验中OvO预测准确率高于OvR
二、MNIST手写数据集
import numpy as np
from sklearn.datasets import fetch_openml
mnist = fetch_openml("mnist_784")x = mnist['data']
y = mnist['target']
print(x.shape)
print(y.shape)
x_train = np.array(x[:60000], dtype=float)
y_train = np.array(y[:60000], dtype=float)
x_test = np.array(x[60000:], dtype=float)
y_test = np.array(y[60000:], dtype=float)from sklearn.linear_model import LogisticRegression2.1 OvR
%%time
lgr1 = LogisticRegression(multi_class='ovr', solver='liblinear')
lgr1.fit(x_train, y_train)
lgr1.score(x_test, y_test)
# 0.9176
2.2 OvO
%%time
lgr2 = LogisticRegression(multi_class='multinomial', solver='newton-cg')
lgr2.fit(x_train, y_train)
lgr2.score(x_test, y_test)
# 0.9208