python人工智能——机器学习——数据的划分和介绍
sklearn数据集
1、数据集划分
2、sklearn数据集接口介绍
3、 sklearn分类数据集
4、 sklearn回归数据集
数据集划分
机器学习一般的数据集会划分为两个部分：
训练数据：用于训练，构建模型
测试数据：在模型检验时使用，用于评估模型是否有效
sklearn数据集接口介绍
sklearn数据集划分API：sklearn.model_selection.train_test_split
scikit-learn数据集API介绍
sklearn.datasets
加载获取流行数据集
datasets.load_*()
获取小规模数据集，数据包含在datasets里
datasets.fetch_*(data_home=None)
获取大规模数据集，需要从网络上下载，函数的第一个参数是data_home，
表示数据集下载的目录,默认是 ~/scikit_learn_data/
sklearn分类数据集
sklearn.datasets.load_iris() 加载并返回鸢尾花数据集
获取数据集返回的类型
load*和fetch*返回的数据类型datasets.base.Bunch(字典格式)
data：特征数据数组，是 [n_samples * n_features] 的二维
numpy.ndarray 数组
target：标签数组，是 n_samples 的一维 numpy.ndarray 数组
DESCR：数据描述
feature_names：特征名,新闻数据，手写数字、回归数据集没有
target_names：标签名,回归数据集没有
from sklearn.datasets import load_iris
li=load_iris()
print("获取特征值")
print(li.data)
print("获取目标值")
print(li.target)
print(li.DESCR)
获取特征值
[[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]
[5.4 3.9 1.7 0.4]
[4.6 3.4 1.4 0.3]
[5. 3.4 1.5 0.2]
[4.4 2.9 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.4 3.7 1.5 0.2]
[4.8 3.4 1.6 0.2]
[4.8 3. 1.4 0.1]
[4.3 3. 1.1 0.1]
[5.8 4. 1.2 0.2]
[5.7 4.4 1.5 0.4]
[5.4 3.9 1.3 0.4]
[5.1 3.5 1.4 0.3]
[5.7 3.8 1.7 0.3]
[5.1 3.8 1.5 0.3]
[5.4 3.4 1.7 0.2]
[5.1 3.7 1.5 0.4]
[4.6 3.6 1. 0.2]
[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[5. 3. 1.6 0.2]
[5. 3.4 1.6 0.4]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[4.7 3.2 1.6 0.2]
[4.8 3.1 1.6 0.2]
[5.4 3.4 1.5 0.4]
[5.2 4.1 1.5 0.1]
[5.5 4.2 1.4 0.2]
[4.9 3.1 1.5 0.2]
[5. 3.2 1.2 0.2]
[5.5 3.5 1.3 0.2]
[4.9 3.6 1.4 0.1]
[4.4 3. 1.3 0.2]
[5.1 3.4 1.5 0.2]
[5. 3.5 1.3 0.3]
[4.5 2.3 1.3 0.3]
[4.4 3.2 1.3 0.2]
[5. 3.5 1.6 0.6]
[5.1 3.8 1.9 0.4]
[4.8 3. 1.4 0.3]
[5.1 3.8 1.6 0.2]
[4.6 3.2 1.4 0.2]
[5.3 3.7 1.5 0.2]
[5. 3.3 1.4 0.2]
[7. 3.2 4.7 1.4]
[6.4 3.2 4.5 1.5]
[6.9 3.1 4.9 1.5]
[5.5 2.3 4. 1.3]
[6.5 2.8 4.6 1.5]
[5.7 2.8 4.5 1.3]
[6.3 3.3 4.7 1.6]
[4.9 2.4 3.3 1. ]
[6.6 2.9 4.6 1.3]
[5.2 2.7 3.9 1.4]
[5. 2. 3.5 1. ]
[5.9 3. 4.2 1.5]
[6. 2.2 4. 1. ]
[6.1 2.9 4.7 1.4]
[5.6 2.9 3.6 1.3]
[6.7 3.1 4.4 1.4]
[5.6 3. 4.5 1.5]
[5.8 2.7 4.1 1. ]
[6.2 2.2 4.5 1.5]
[5.6 2.5 3.9 1.1]
[5.9 3.2 4.8 1.8]
[6.1 2.8 4. 1.3]
[6.3 2.5 4.9 1.5]
[6.1 2.8 4.7 1.2]
[6.4 2.9 4.3 1.3]
[6.6 3. 4.4 1.4]
[6.8 2.8 4.8 1.4]
[6.7 3. 5. 1.7]
[6. 2.9 4.5 1.5]
[5.7 2.6 3.5 1. ]
[5.5 2.4 3.8 1.1]
[5.5 2.4 3.7 1. ]
[5.8 2.7 3.9 1.2]
[6. 2.7 5.1 1.6]
[5.4 3. 4.5 1.5]
[6. 3.4 4.5 1.6]
[6.7 3.1 4.7 1.5]
[6.3 2.3 4.4 1.3]
[5.6 3. 4.1 1.3]
[5.5 2.5 4. 1.3]
[5.5 2.6 4.4 1.2]
[6.1 3. 4.6 1.4]
[5.8 2.6 4. 1.2]
[5. 2.3 3.3 1. ]
[5.6 2.7 4.2 1.3]
[5.7 3. 4.2 1.2]
[5.7 2.9 4.2 1.3]
[6.2 2.9 4.3 1.3]
[5.1 2.5 3. 1.1]
[5.7 2.8 4.1 1.3]
[6.3 3.3 6. 2.5]
[5.8 2.7 5.1 1.9]
[7.1 3. 5.9 2.1]
[6.3 2.9 5.6 1.8]
[6.5 3. 5.8 2.2]
[7.6 3. 6.6 2.1]
[4.9 2.5 4.5 1.7]
[7.3 2.9 6.3 1.8]
[6.7 2.5 5.8 1.8]
[7.2 3.6 6.1 2.5]
[6.5 3.2 5.1 2. ]
[6.4 2.7 5.3 1.9]
[6.8 3. 5.5 2.1]
[5.7 2.5 5. 2. ]
[5.8 2.8 5.1 2.4]
[6.4 3.2 5.3 2.3]
[6.5 3. 5.5 1.8]
[7.7 3.8 6.7 2.2]
[7.7 2.6 6.9 2.3]
[6. 2.2 5. 1.5]
[6.9 3.2 5.7 2.3]
[5.6 2.8 4.9 2. ]
[7.7 2.8 6.7 2. ]
[6.3 2.7 4.9 1.8]
[6.7 3.3 5.7 2.1]
[7.2 3.2 6. 1.8]
[6.2 2.8 4.8 1.8]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.1]
[7.2 3. 5.8 1.6]
[7.4 2.8 6.1 1.9]
[7.9 3.8 6.4 2. ]
[6.4 2.8 5.6 2.2]
[6.3 2.8 5.1 1.5]
[6.1 2.6 5.6 1.4]
[7.7 3. 6.1 2.3]
[6.3 3.4 5.6 2.4]
[6.4 3.1 5.5 1.8]
[6. 3. 4.8 1.8]
[6.9 3.1 5.4 2.1]
[6.7 3.1 5.6 2.4]
[6.9 3.1 5.1 2.3]
[5.8 2.7 5.1 1.9]
[6.8 3.2 5.9 2.3]
[6.7 3.3 5.7 2.5]
[6.7 3. 5.2 2.3]
[6.3 2.5 5. 1.9]
[6.5 3. 5.2 2. ]
[6.2 3.4 5.4 2.3]
[5.9 3. 5.1 1.8]]
获取目标值
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
.. _iris_dataset:
Iris plants dataset
--------------------
**Data Set Characteristics:**
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
:Summary Statistics:
============== ==== ==== ======= ===== ====================
Min Max Mean SD Class Correlation
============== ==== ==== ======= ===== ====================
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
============== ==== ==== ======= ===== ====================
:Missing Attribute Values: None
:Class Distribution: 33.3% for each of 3 classes.
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%[email protected])
:Date: July, 1988
The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.
This is perhaps the best known database to be found in the
pattern recognition literature. Fisher's paper is a classic in the field and
is referenced frequently to this day. (See Duda & Hart, for example.) The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant. One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.
.. topic:: References
- Fisher, R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
Mathematical Statistics" (John Wiley, NY, 1950).
- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
on Information Theory, May 1972, 431-433.
- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
- Many, many more ...
数据集进行分割
sklearn.model_selection.train_test_split(*arrays, **options)
x 数据集的特征值
y 数据集的标签值
test_size 测试集的大小，一般为float
random_state 随机数种子,不同的种子会造成不同的随机
采样结果。相同的种子采样结果相同。
return 训练集特征值，测试集特征值，训练标签，测试标签(默认随机取)
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
li=load_iris()
#注意返回值，既包含训练集也包含测试集
x_train,x_test,y_train,y_test=train_test_split(li.data,li.target,test_size=0.25)
print("训练集特征值和目标值",x_train,y_train)
print("测试集特征值和目标值",x_test,y_test)
训练集特征值和目标值 [[5. 2.3 3.3 1. ]
[6.7 3.1 5.6 2.4]
[6. 2.7 5.1 1.6]
[6.3 3.3 6. 2.5]
[6. 2.2 4. 1. ]
[4.9 3.1 1.5 0.1]
[7.7 2.6 6.9 2.3]
[4.3 3. 1.1 0.1]
[5.8 2.7 5.1 1.9]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[5. 3.5 1.3 0.3]
[5.1 3.5 1.4 0.3]
[5.5 2.5 4. 1.3]
[5.1 3.3 1.7 0.5]
[5.1 3.8 1.9 0.4]
[6. 2.9 4.5 1.5]
[5.8 2.7 3.9 1.2]
[5.4 3.9 1.7 0.4]
[5.7 2.9 4.2 1.3]
[6.3 2.5 4.9 1.5]
[6.7 3.1 4.7 1.5]
[6.4 2.7 5.3 1.9]
[5.1 3.4 1.5 0.2]
[4.9 2.4 3.3 1. ]
[6.3 2.5 5. 1.9]
[5.8 4. 1.2 0.2]
[5.4 3.7 1.5 0.2]
[6.2 2.9 4.3 1.3]
[6.1 2.9 4.7 1.4]
[6.9 3.2 5.7 2.3]
[5. 3.4 1.6 0.4]
[6.4 3.1 5.5 1.8]
[7. 3.2 4.7 1.4]
[4.6 3.6 1. 0.2]
[5.9 3. 4.2 1.5]
[5.6 3. 4.5 1.5]
[7.7 2.8 6.7 2. ]
[5.8 2.6 4. 1.2]
[4.4 3. 1.3 0.2]
[4.6 3.4 1.4 0.3]
[5.1 3.8 1.5 0.3]
[6.6 3. 4.4 1.4]
[5.7 4.4 1.5 0.4]
[6.4 2.8 5.6 2.1]
[6.9 3.1 5.1 2.3]
[5.6 2.7 4.2 1.3]
[7.3 2.9 6.3 1.8]
[4.7 3.2 1.6 0.2]
[4.8 3.4 1.6 0.2]
[5. 3.2 1.2 0.2]
[5.6 3. 4.1 1.3]
[5.5 2.4 3.8 1.1]
[4.8 3. 1.4 0.1]
[5.1 3.7 1.5 0.4]
[5. 3.6 1.4 0.2]
[7.7 3.8 6.7 2.2]
[4.8 3.1 1.6 0.2]
[5.9 3. 5.1 1.8]
[5.7 2.6 3.5 1. ]
[6.4 3.2 5.3 2.3]
[5.8 2.8 5.1 2.4]
[4.4 3.2 1.3 0.2]
[5. 3.3 1.4 0.2]
[6.5 3.2 5.1 2. ]
[5.1 3.5 1.4 0.2]
[6.5 3. 5.8 2.2]
[6.1 2.6 5.6 1.4]
[7.2 3.6 6.1 2.5]
[5.5 2.4 3.7 1. ]
[5.8 2.7 5.1 1.9]
[7.7 3. 6.1 2.3]
[5. 3. 1.6 0.2]
[6.9 3.1 5.4 2.1]
[7.1 3. 5.9 2.1]
[5.4 3.4 1.7 0.2]
[6.1 2.8 4. 1.3]
[5.3 3.7 1.5 0.2]
[7.2 3. 5.8 1.6]
[6.2 2.8 4.8 1.8]
[5.4 3.4 1.5 0.4]
[7.4 2.8 6.1 1.9]
[6.7 3.3 5.7 2.1]
[5.7 3.8 1.7 0.3]
[5.6 2.5 3.9 1.1]
[4.8 3.4 1.9 0.2]
[6.7 3. 5. 1.7]
[6.5 2.8 4.6 1.5]
[4.9 3. 1.4 0.2]
[4.5 2.3 1.3 0.3]
[5.5 2.6 4.4 1.2]
[6.1 3. 4.6 1.4]
[6.4 2.8 5.6 2.2]
[4.9 3.1 1.5 0.2]
[6.3 3.4 5.6 2.4]
[6. 3. 4.8 1.8]
[5.2 4.1 1.5 0.1]
[5.7 2.8 4.1 1.3]
[7.9 3.8 6.4 2. ]
[4.7 3.2 1.3 0.2]
[6.3 2.8 5.1 1.5]
[4.8 3. 1.4 0.3]
[5.7 2.5 5. 2. ]
[5.7 2.8 4.5 1.3]
[6.4 2.9 4.3 1.3]
[4.9 3.6 1.4 0.1]
[5. 3.5 1.6 0.6]
[6.8 2.8 4.8 1.4]
[5.5 4.2 1.4 0.2]
[5.8 2.7 4.1 1. ]
[5.7 3. 4.2 1.2]
[6.3 2.9 5.6 1.8]] [1 2 1 2 1 0 2 0 2 0 0 0 0 1 0 0 1 1 0 1 1 1 2 0 1 2 0 0 1 1 2 0 2 1 0 1 1
2 1 0 0 0 1 0 2 2 1 2 0 0 0 1 1 0 0 0 2 0 2 1 2 2 0 0 2 0 2 2 2 1 2 2 0 2
2 0 1 0 2 2 0 2 2 0 1 0 1 1 0 0 1 1 2 0 2 2 0 1 2 0 2 0 2 1 1 0 0 1 0 1 1
2]
测试集特征值和目标值 [[6.2 2.2 4.5 1.5]
[6.7 3. 5.2 2.3]
[6.9 3.1 4.9 1.5]
[6.4 3.2 4.5 1.5]
[4.6 3.2 1.4 0.2]
[4.9 2.5 4.5 1.7]
[5.6 2.9 3.6 1.3]
[6.3 2.7 4.9 1.8]
[7.6 3. 6.6 2.1]
[4.4 2.9 1.4 0.2]
[5. 2. 3.5 1. ]
[6.2 3.4 5.4 2.3]
[6.5 3. 5.2 2. ]
[6.1 3. 4.9 1.8]
[6.6 2.9 4.6 1.3]
[6.3 3.3 4.7 1.6]
[6.3 2.3 4.4 1.3]
[6.8 3.2 5.9 2.3]
[6.8 3. 5.5 2.1]
[5.1 2.5 3. 1.1]
[4.6 3.1 1.5 0.2]
[5.5 2.3 4. 1.3]
[6.7 3.1 4.4 1.4]
[6.1 2.8 4.7 1.2]
[6. 3.4 4.5 1.6]
[5.2 2.7 3.9 1.4]
[5.1 3.8 1.6 0.2]
[6.5 3. 5.5 1.8]
[5.5 3.5 1.3 0.2]
[5. 3.4 1.5 0.2]
[6.7 2.5 5.8 1.8]
[5.6 2.8 4.9 2. ]
[5.9 3.2 4.8 1.8]
[5.4 3. 4.5 1.5]
[5.4 3.9 1.3 0.4]
[7.2 3.2 6. 1.8]
[6. 2.2 5. 1.5]
[6.7 3.3 5.7 2.5]] [1 2 1 1 0 2 1 2 2 0 1 2 2 2 1 1 1 2 2 1 0 1 1 1 1 1 0 2 0 0 2 2 1 1 0 2 2
2]
用于分类的大数据集
sklearn.datasets.fetch_20newsgroups(data_home=None,subset=‘train’)
subset: 'train'或者'test','all'，可选，选择要加载的数据集.
训练集的“训练”，测试集的“测试”，两者的“全部”
datasets.clear_data_home(data_home=None)
清除目录下的数据
sklearn回归数据集
sklearn.datasets.load_boston() 加载并返回波士顿房价数据集
from sklearn.datasets import load_iris,load_boston
lb = load_boston()
print("获取特征值")
print(lb.data)
print("目标值")
print(lb.target)
print(lb.DESCR)
获取特征值
[[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]
[2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]
[2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]
...
[6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]
[1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]
[4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]
目标值
[24. 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15. 18.9 21.7 20.4
18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8
18.4 21. 12.7 14.5 13.2 13.1 13.5 18.9 20. 21. 24.7 30.8 34.9 26.6
25.3 24.7 21.2 19.3 20. 16.6 14.4 19.4 19.7 20.5 25. 23.4 18.9 35.4
24.7 31.6 23.3 19.6 18.7 16. 22.2 25. 33. 23.5 19.4 22. 17.4 20.9
24.2 21.7 22.8 23.4 24.1 21.4 20. 20.8 21.2 20.3 28. 23.9 24.8 22.9
23.9 26.6 22.5 22.2 23.6 28.7 22.6 22. 22.9 25. 20.6 28.4 21.4 38.7
43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8
18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22. 20.3 20.5 17.3 18.8 21.4
15.7 16.2 18. 14.3 19.2 19.6 23. 18.4 15.6 18.1 17.4 17.1 13.3 17.8
14. 14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4
17. 15.6 13.1 41.3 24.3 23.3 27. 50. 50. 50. 22.7 25. 50. 23.8
23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2
37.9 32.5 26.4 29.6 50. 32. 29.8 34.9 37. 30.5 36.4 31.1 29.1 50.
33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50. 22.6 24.4 22.5 24.4 20.
21.7 19.3 22.4 28.1 23.7 25. 23.3 28.7 21.5 23. 26.7 21.7 27.5 30.1
44.8 50. 37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29. 24. 25.1 31.5
23.7 23.3 22. 20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8
29.6 42.8 21.9 20.9 44. 50. 36. 30.1 33.8 43.1 48.8 31. 36.5 22.8
30.7 50. 43.5 20.7 21.1 25.2 24.4 35.2 32.4 32. 33.2 33.1 29.1 35.1
45.4 35.4 46. 50. 32.2 22. 20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9
21.7 28.6 27.1 20.3 22.5 29. 24.8 22. 26.4 33.1 36.1 28.4 33.4 28.2
22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21. 23.8 23.1
20.4 18.5 25. 24.6 23. 22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1
19.5 18.5 20.6 19. 18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6
22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25. 19.9 20.8 16.8
21.9 27.5 21.9 23.1 50. 50. 50. 50. 50. 13.8 13.8 15. 13.9 13.3
13.1 10.2 10.4 10.9 11.3 12.3 8.8 7.2 10.5 7.4 10.2 11.5 15.1 23.2
9.7 13.8 12.7 13.1 12.5 8.5 5. 6.3 5.6 7.2 12.1 8.3 8.5 5.
11.9 27.9 17.2 27.5 15. 17.2 17.9 16.3 7. 7.2 7.5 10.4 8.8 8.4
16.7 14.2 20.8 13.4 11.7 8.3 10.2 10.9 11. 9.5 14.5 14.1 16.1 14.3
11.7 13.4 9.6 8.7 8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6
14.1 13. 13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20. 16.4 17.7
19.5 20.2 21.4 19.9 19. 19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3
16.7 12. 14.6 21.4 23. 23.7 25. 21.8 20.6 21.2 19.1 20.6 15.2 7.
8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9
22. 11.9]
.. _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.
.. topic:: 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.