多项逻辑回归
多项逻辑回归详细操作教程
逻辑回归的另一种有用形式是多项逻辑回归，其中目标或因变量可以具有3种或多种可能的
无序 类型，即没有定量意义的类型。
Python的实现
现在，我们将在Python中实现上述多项式逻辑回归的概念。为此，我们使用来自sklearn的名为
digit 的数据集。
首先，我们需要按如下所示导入必要的库-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
import sklearn
from sklearn import datasets
from sklearn import linear_model
from sklearn import metrics
from sklearn.model_selection import train_test_split
接下来，我们需要加载数字数据集-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
digits = datasets.load_digits()
现在，如下定义特征矩阵（X）和响应向量（y）-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
X = digits.data
y = digits.target
借助下一行代码，我们可以将X和y分为训练和测试集-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 1)
现在创建如下的逻辑回归对象-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
digreg = linear_model.LogisticRegression()
现在，我们需要使用如下训练集来训练模型-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
digreg.fit(X_train, y_train)
接下来，对测试集进行如下预测-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
y_pred = digreg.predict(X_test)
接下来按如下所示打印模型的精度-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
print("Accuracy of Logistic Regression model is:",
metrics.accuracy_score(y_test, y_pred)*100)
输出
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
Accuracy of Logistic Regression model is: 95.6884561891516
从上面的输出中，我们可以看到我们模型的准确性约为96％。
