python实现多项式逻辑回归

（1）导包

#第一部分：导包
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.datasets import make_classification

（2）生成数据集

#第二部分：生成数据集
np.random.seed(0)
x=np.random.normal(0,1,size=(200,2))
y=np.array((x[:,0]**2)+(x[:,1]**2)<2,dtype='int')#圆形区域的判断

（3）划分数据集

#第三部分：划分数据集
x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.7,random_state=233,stratify=y)
plt.scatter(x_train[:,0],x_train[:,1],c=y_train)
plt.show()
#开始使用逻辑回归
from sklearn.linear_model import LogisticRegression
'''
clif=LogisticRegression()
clif.fit(x_train,y_train)
score=clif.score(x_train,y_train)
print(score)
'''

（4）多项式逻辑回归

#第四部分：开始使用多项式回归
from sklearn.preprocessing import PolynomialFeatures
poly=PolynomialFeatures(degree=2)
poly.fit(x_train,y_train)
x_train_poly=poly.transform(x_train)
x_test_poly=poly.transform(x_test)
clif=LogisticRegression()
clif.fit(x_train_poly,y_train)
#训练集的评分
score=clif.score(x_train_poly,y_train)
print("训练集的评分=",score)
#测试集的评分
score=clif.score(x_test_poly,y_test)
print("测试集的评分=",score)

（5）完整pycharm代码

#第一部分：导包
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.datasets import make_classification
#第二部分：生成数据集
np.random.seed(0)
x=np.random.normal(0,1,size=(200,2))
y=np.array((x[:,0]**2)+(x[:,1]**2)<2,dtype='int')#圆形区域的判断
#第三部分：划分数据集
x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.7,random_state=233,stratify=y)
plt.scatter(x_train[:,0],x_train[:,1],c=y_train)
plt.show()
#开始使用逻辑回归
from sklearn.linear_model import LogisticRegression
'''
clif=LogisticRegression()
clif.fit(x_train,y_train)
score=clif.score(x_train,y_train)
print(score)
'''
#第四部分：开始使用多项式回归
from sklearn.preprocessing import PolynomialFeatures
poly=PolynomialFeatures(degree=2)
poly.fit(x_train,y_train)
x_train_poly=poly.transform(x_train)
x_test_poly=poly.transform(x_test)
clif=LogisticRegression()
clif.fit(x_train_poly,y_train)
#训练集的评分
score=clif.score(x_train_poly,y_train)
print("训练集的评分=",score)
#测试集的评分
score=clif.score(x_test_poly,y_test)
print("测试集的评分=",score)