logitic回归线性处理代码

from sklearn.datasets import load_iris
import  matplotlib.pyplot as plt
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
iris =load_iris()
X=iris.data
Y=iris.target
x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=0)
lr=LogisticRegression()
lr.fit(x_train,y_train)
print("模型训练集的准确率:%.3f"%lr.score(x_train,y_train))
print("模型测试集的准确率:%.3f"%lr.score(x_test,y_test))
target_names =['setosa','versicolor','virginica']
y_hat=lr.predict(x_test)
print(classification_report(y_test,y_hat,target_names=target_names))
结果：

模型训练集的准确率:0.981
模型测试集的准确率:0.978
              precision    recall  f1-score   support

      setosa       1.00      1.00      1.00        16
  versicolor       1.00      0.94      0.97        18
   virginica       0.92      1.00      0.96        11

    accuracy                           0.98        45
   macro avg       0.97      0.98      0.98        45
weighted avg       0.98      0.98      0.98        45