加入L2

好的，以下是加入L2正则化的程序示例：

import pandas as pd 
import numpy as np 
from sklearn.linear_model import Ridge 
from sklearn.preprocessing import StandardScaler 
from sklearn.preprocessing import PolynomialFeatures 
from sklearn.feature_selection import SelectKBest 
from sklearn.feature_selection import f_regression 
from sklearn.model_selection import train_test_split  

# 读取 Excel 文件 
data = pd.read_excel('D://数据1.xlsx', sheet_name='000')  

# 把数据分成输入和输出 
X = data.iloc[:, 0:4].values 
y = data.iloc[:, 0:4].values  

# 标准化处理 
scaler = StandardScaler() 
X = scaler.fit_transform(X)  

# 添加多项式特征 
poly = PolynomialFeatures(degree=2, include_bias=False) 
X = poly.fit_transform(X)    

# 将数据分为训练集和测试集 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)  

# 创建岭回归模型, 加入L2正则化
model = Ridge(alpha=0.88, solver='auto', normalize=False, max_iter=1000, tol=0.001, random_state=None, 
              penalty='l2',  # 加入L2正则化
              fit_intercept=True)

# 拟合模型 
model.fit(X_train, y_train)  

# 使用模型进行预测 
y_pred = model.predict(X_test)  

# 将预测结果四舍五入取整 
y_pred = np.round(y_pred)  

# 去除重复行 
y_pred = np.unique(y_pred, axis=0)  

# 打印预测结果 
print(y_pred)

在创建Ridge模型时，设置penalty参数为'l2'即可加入L2正则化。其他参数的含义可以参考sklearn官方文档。