从 sklearn 数据集库导入波士顿房价数据集( boston ）

在Python的sklearn库中，你可以直接使用`load_boston()`函数来导入波士顿房价数据集。这个数据集包含了马萨诸塞州波士顿地区的房屋价格信息，包括诸如犯罪率、人口密度、距离就业中心的距离等多个特征，目的是预测每个区域的房屋价值。

以下是导入数据的代码示例：

from sklearn.datasets import load_boston

# 加载波士顿房价数据集
boston_dataset = load_boston()
boston = boston_dataset.data  # 获取特征数据
target = boston_dataset.target  # 获取目标变量，即房价

# 查看数据集的一些基本信息
print("Boston Housing dataset:")
print("Features: ", boston_dataset.feature_names)
print("Shape of data: ", boston.shape)
print("Target variable: ", target.shape)

在这个数据集中，`boston`是一个二维数组，每一行代表一个样本，每列对应一个特征。而`target`则是对应的房屋价格标签。

相关问题

sklearn导入波士顿房价数据集

sklearn中自带了波士顿房价数据集，可以通过以下代码导入：

from sklearn.datasets import load_boston

boston = load_boston()

X = boston.data # 特征矩阵
y = boston.target # 目标向量

其中，X是一个13维的特征矩阵，y是一个样本数量为506的目标向量。可以通过以下代码查看数据集的描述：

print(boston.DESCR)

输出结果如下：

.. _boston_dataset:

Boston house prices dataset
---------------------------

**Data Set Characteristics:**  

    :Number of Instances: 506 

    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.

    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - AGE      proportion of owner-occupied units built prior to 1940
        - DIS      weighted distances to five Boston employment centres
        - RAD      index of accessibility to radial highways
        - TAX      full-value property-tax rate per $10,000
        - PTRATIO  pupil-teacher ratio by town
        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
        - LSTAT    % lower status of the population
        - MEDV     Median value of owner-occupied homes in $1000's

    :Missing Attribute Values: None

    :Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset.
https://archive.ics.uci.edu/ml/machine-learning-databases/housing/


This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980.   N.B. Various transformations are used in the table on pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression problems.
     
**References**
- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

调用sklearn库中波士顿房价数据集，将波士顿房价数据集划分为训练集和测试集，利用ridge回归预测测试集房价

好的，以下是代码实现：

# 导入所需库和数据集
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error

boston = load_boston()

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42)

# 构建Ridge回归模型
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)

# 对测试集进行预测
y_pred = ridge.predict(X_test)

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

输出结果：

测试集均方误差: 24.291119474973616

以上代码实现了利用Ridge回归对波士顿房价数据集进行预测，并计算了测试集的均方误差。