sklearn之逻辑回归
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2023-12-26 23:09:03
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逻辑回归
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report # 评估分类结果的指标
from sklearn import preprocessing
from sklearn import linear_model
# 数据是否需要标准化
scale = False
# scale = True
# 导入数据
data = np.genfromtxt("C:\\ML\\chapter-1\\LR-testSet.csv",delimiter=",") # 这种方法只能读取数字,不能读取字符,字符会变成nan
# print(data)
x_data = data[:,:-1]
y_data = data[:,-1]
def plot():
x0 = []
x1 = []
y0 = []
y1 = []
# 切分不同类型的数据
for i in range(len(x_data)):
if y_data[i] == 0:
x0.append(x_data[i,0])
y0.append(x_data[i,1])
else:
x1.append(x_data[i,0])
y1.append(x_data[i,1])
# 画图 散点图
scatter0 = plt.scatter(x0,y0,c='b',marker='o')
scatter1 = plt.scatter(x1,y1,c='r',marker='x')
# 画图例
plt.legend(handles=[scatter0,scatter1],labels=['label0','label1'],loc='best') # loc='best'会自动把图例放在最好的位置
# plot()
# plt.show()
logistic = linear_model.LogisticRegression()
logistic.fit(x_data,y_data)
print('相关系数:',logistic.coef_)
if scale == False:
# 画图决策边界
plot()
x_test = np.array([[-4],[3]])
y_test = (-logistic.intercept_-x_test*logistic.coef_[0][0])/logistic.coef_[0][1]
plt.plot(x_test,y_test,'k')
plt.show()
predictions = logistic.predict(x_data)
print(classification_report(y_data,predictions))
结果如下: