data_RAD数据集

data_RAD数据集是一个基于RAD（Restriction site Associated DNA）技术的基因组学数据集。该数据集包含了多个物种的RAD序列数据，是研究基因组结构、遗传多样性、种群遗传学等方面的重要资源。

data_RAD数据集的制备基于RAD技术，通过在基因组DNA中选择特定的限制性内切酶切割位点，并使用PCR扩增特定长度的DNA片段，然后进行高通量测序。这种方法可以产生高度可重复性的DNA序列，同时可以有效地筛选出基因组中的多态性位点。

data_RAD数据集的应用范围非常广泛，包括种群遗传学、遗传多样性、进化生物学、生态学、物种鉴定、基因组结构等领域。该数据集的发布和共享，将进一步促进基因组学研究的发展，推动生物学的进步。

相关问题

pandas读取boston_house_prices数据集

好的，可以使用以下代码读取boston_house_prices数据集：

import pandas as pd

# 读取数据集
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', 
                 header=None, sep='\s+')
# 设置列名
df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 
              'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']

# 显示前5行数据
print(df.head())

注意：`header=None` 表示数据集没有列名，需要手动设置；`sep='\s+'` 表示数据集是以空格为分隔符的，需要进行分隔。

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.