岭回归与lasoo回归python实现的全代码

当然可以！下面是岭回归（Ridge Regression）和Lasso回归（Lasso Regression）的Python实现代码：

岭回归：

from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np

# 生成示例数据
np.random.seed(0)
X = np.random.rand(100, 10)
y = np.random.rand(100)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 创建岭回归模型
ridge = Ridge(alpha=1.0)

# 拟合模型
ridge.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = ridge.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差：", mse)

Lasso回归：

from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np

# 生成示例数据
np.random.seed(0)
X = np.random.rand(100, 10)
y = np.random.rand(100)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 创建Lasso回归模型
lasso = Lasso(alpha=1.0)

# 拟合模型
lasso.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = lasso.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差：", mse)

以上是岭回归和Lasso回归的Python实现代码。你可以根据实际情况，修改数据和超参数，运行代码进行训练和预测。希望对你有帮助！