在python中对波士顿犯罪数据集进行套索回归

from sklearn import datasets
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split

# 导入波士顿犯罪数据集
boston = datasets.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)

lasso = Lasso(alpha=0.1) # alpha为正则化强度，可根据需要进行调整
lasso.fit(X_train, y_train)

y_pred = lasso.predict(X_test)

# 可以使用各种评估指标来评估模型的性能
# 这里以均方误差（Mean Squared Error）为例
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, y_pred)
print("均方误差：", mse)