从 sklearn 数据集库导入波士顿房价数据集( boston )
时间: 2024-10-21 07:05:51 浏览: 433
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:
```python
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
我如何从sklearn导入并加载波士顿房价数据集?
在Python中,如果你想要从scikit-learn库导入并加载波士顿房价数据集,可以按照以下几个步骤操作:
首先,你需要确保已经安装了`scikit-learn`库。如果没有,可以使用pip安装:
```bash
pip install scikit-learn
```
然后,你可以通过下面的代码来加载波士顿房价数据集:
```python
from sklearn.datasets import load_boston
# 加载数据集
boston = load_boston()
```
`load_boston()`函数会返回一个字典,包含了数据集的各种信息,如特征数组、目标变量数组、特征名称等。你可以分别访问这些内容,例如:
```python
# 特征数据(输入)
X = boston.data
# 目标变量(房价)
y = boston.target
# 数据集描述
print(boston.DESCR)
```
如果你想查看特征名,可以这样做:
```python
# 特征名称
feature_names = boston.feature_names
```
这就是加载波士顿房价数据的基本过程。
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.
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