使用sklearn自带的岭回归和Lasso回归对波士顿房价数据集进行回归预测

好的，我可以为您提供一个简单的代码示例。

首先，我们需要导入波士顿房价数据集和所需的库：

from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge, Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

接着，我们可以加载波士顿房价数据集：

boston = load_boston()
X = boston.data
y = boston.target

然后，我们将数据集拆分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

现在我们可以使用岭回归和Lasso回归来训练模型并进行预测：

# 岭回归
ridge = Ridge(alpha=0.1)
ridge.fit(X_train, y_train)
ridge_pred = ridge.predict(X_test)

# Lasso回归
lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)
lasso_pred = lasso.predict(X_test)

最后，我们可以计算均方根误差（RMSE）来评估模型的性能：

print("Ridge regression RMSE: ", mean_squared_error(y_test, ridge_pred, squared=False))
print("Lasso regression RMSE: ", mean_squared_error(y_test, lasso_pred, squared=False))

完整代码如下：

from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge, Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

boston = load_boston()
X = boston.data
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

ridge = Ridge(alpha=0.1)
ridge.fit(X_train, y_train)
ridge_pred = ridge.predict(X_test)

lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)
lasso_pred = lasso.predict(X_test)

print("Ridge regression RMSE: ", mean_squared_error(y_test, ridge_pred, squared=False))
print("Lasso regression RMSE: ", mean_squared_error(y_test, lasso_pred, squared=False))

希望这可以帮助您开始使用岭回归和Lasso回归预测波士顿房价数据集。