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scipy版本和python版本之间的关系 scipy和sklearn

1.了解sklearn

https://scikit-learn.org/stable/ 此为(基于 Python 语言建立在 NumPy ,SciPy 和 matplotlib 上的机器学习工具的网址)
先了解一下库
NumPy:NumPy(Numerical Python) 是 Python 语言的一个扩展程序库,支持大量的维度数组与矩阵运算,此外也针对数组运算提供大量的数学函数库。。

SciPy :scipy是一个用于数学、科学、工程领域的常用软件包,可以处理插值、积分、优化、图像处理、常微分方程数值解的求解、信号处理等问题

matplotlib :Matplotlib 是 Python 的绘图库,仅需要几行代码,便可以生成直方图、功率谱、条形图、错误图、散点图等。 它可与 NumPy 一起使用,提供了一种有效的 MatLab 开源替代方案。 它也可以和图形工具包一起使用,如 PyQt 和 wxPython。

.

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机器学习有很的算法 在面对问题时,在算法之中,要能做出选择:

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python,第1张

scikit-learn包含众多顶级机器学习算法,主要有六大基本功能,分别是分类、回归、聚类、数据降维、模型选择和数据预处理

有一个安全的 numpy 和 scipy,安装 scikit-learn 最简单的方法是使用pip:
pip install -U scikit-learn

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_人工智能_02,第2张

!pip list#此个操作可以查看你安装的库
Package                            Version
---------------------------------- -------------------
alabaster                          0.7.12
anaconda-client                    1.7.2
anaconda-navigator                 2.0.3
anaconda-project                   0.9.1
anyio                              2.2.0
appdirs                            1.4.4
argh                               0.26.2
argon2-cffi                        20.1.0
asn1crypto                         1.4.0
astroid                            2.5
astropy                            4.2.1
async-generator                    1.10
atomicwrites                       1.4.0
attrs                              20.3.0
autopep8                           1.5.6
Babel                              2.9.0
backcall                           0.2.0
backports.functools-lru-cache      1.6.4
backports.shutil-get-terminal-size 1.0.0
backports.tempfile                 1.0
backports.weakref                  1.0.post1
bcrypt                             3.2.0
beautifulsoup4                     4.9.3
bitarray                           1.9.2
bkcharts                           0.2
black                              19.10b0
bleach                             3.3.0
bokeh                              2.3.2
boto                               2.49.0
Bottleneck                         1.3.2
brotlipy                           0.7.0
certifi                            2020.12.5
cffi                               1.14.5
chardet                            4.0.0
click                              7.1.2
cloudpickle                        1.6.0
clyent                             1.2.2
colorama                           0.4.4
comtypes                           1.1.9
conda                              4.10.1
conda-build                        3.21.4
conda-content-trust                0+unknown
conda-package-handling             1.7.3
conda-repo-cli                     1.0.4
conda-token                        0.3.0
conda-verify                       3.4.2
contextlib2                        0.6.0.post1
cryptography                       3.4.7
cycler                             0.10.0
Cython                             0.29.23
cytoolz                            0.11.0
dask                               2021.4.0
decorator                          5.0.6
defusedxml                         0.7.1
diff-match-patch                   20200713
distributed                        2021.4.0
docutils                           0.17
entrypoints                        0.3
et-xmlfile                         1.0.1
fastcache                          1.1.0
filelock                           3.0.12
flake8                             3.9.0
Flask                              1.1.2
fsspec                             0.9.0
future                             0.18.2
gevent                             21.1.2
glob2                              0.7
greenlet                           1.0.0
h5py                               2.10.0
HeapDict                           1.0.1
html5lib                           1.1
idna                               2.10
imagecodecs                        2021.3.31
imageio                            2.9.0
imagesize                          1.2.0
importlib-metadata                 3.10.0
iniconfig                          1.1.1
intervaltree                       3.1.0
ipykernel                          5.3.4
ipython                            7.22.0
ipython-genutils                   0.2.0
ipywidgets                         7.6.3
isort                              5.8.0
itsdangerous                       1.1.0
jdcal                              1.4.1
jedi                               0.17.2
Jinja2                             2.11.3
joblib                             1.0.1
json5                              0.9.5
jsonschema                         3.2.0
jupyter                            1.0.0
jupyter-client                     6.1.12
jupyter-console                    6.4.0
jupyter-core                       4.7.1
jupyter-packaging                  0.7.12
jupyter-server                     1.4.1
jupyterlab                         3.0.14
jupyterlab-pygments                0.1.2
jupyterlab-server                  2.4.0
jupyterlab-widgets                 1.0.0
keyring                            22.3.0
kiwisolver                         1.3.1
lazy-object-proxy                  1.6.0
libarchive-c                       2.9
llvmlite                           0.36.0
locket                             0.2.1
lxml                               4.6.3
MarkupSafe                         1.1.1
matplotlib                         3.3.4
mccabe                             0.6.1
menuinst                           1.4.16
mistune                            0.8.4
mkl-fft                            1.3.0
mkl-random                         1.2.1
mkl-service                        2.3.0
mock                               4.0.3
more-itertools                     8.7.0
mpmath                             1.2.1
msgpack                            1.0.2
multipledispatch                   0.6.0
mypy-extensions                    0.4.3
navigator-updater                  0.2.1
nbclassic                          0.2.6
nbclient                           0.5.3
nbconvert                          6.0.7
nbformat                           5.1.3
nest-asyncio                       1.5.1
networkx                           2.5
nltk                               3.6.1
nose                               1.3.7
notebook                           6.3.0
numba                              0.53.1
numexpr                            2.7.3
numpy                              1.20.1
numpydoc                           1.1.0
olefile                            0.46
openpyxl                           3.0.7
packaging                          20.9
pandas                             1.2.4
pandocfilters                      1.4.3
paramiko                           2.7.2
parso                              0.7.0
partd                              1.2.0
path                               15.1.2
pathlib2                           2.3.5
pathspec                           0.7.0
patsy                              0.5.1
pep8                               1.7.1
pexpect                            4.8.0
pickleshare                        0.7.5
Pillow                             8.2.0
pip                                21.0.1
pkginfo                            1.7.0
pluggy                             0.13.1
ply                                3.11
prometheus-client                  0.10.1
prompt-toolkit                     3.0.17
psutil                             5.8.0
ptyprocess                         0.7.0
py                                 1.10.0
pycodestyle                        2.6.0
pycosat                            0.6.3
pycparser                          2.20
pycurl                             7.43.0.6
pydocstyle                         6.0.0
pyerfa                             1.7.3
pyflakes                           2.2.0
Pygments                           2.8.1
pylint                             2.7.4
pyls-black                         0.4.6
pyls-spyder                        0.3.2
PyNaCl                             1.4.0
pyodbc                             4.0.0-unsupported
pyOpenSSL                          20.0.1
pyparsing                          2.4.7
pyreadline                         2.1
pyrsistent                         0.17.3
PySocks                            1.7.1
pytest                             6.2.3
python-dateutil                    2.8.1
python-jsonrpc-server              0.4.0
python-language-server             0.36.2
pytz                               2021.1
PyWavelets                         1.1.1
pywin32                            227
pywin32-ctypes                     0.2.0
pywinpty                           0.5.7
PyYAML                             5.4.1
pyzmq                              20.0.0
QDarkStyle                         2.8.1
QtAwesome                          1.0.2
qtconsole                          5.0.3
QtPy                               1.9.0
regex                              2021.4.4
requests                           2.25.1
rope                               0.18.0
Rtree                              0.9.7
ruamel-yaml-conda                  0.15.100
scikit-image                       0.18.1
scikit-learn                       0.24.1
scipy                              1.6.2
seaborn                            0.11.1
Send2Trash                         1.5.0
setuptools                         52.0.0.post20210125
simplegeneric                      0.8.1
singledispatch                     0.0.0
sip                                4.19.13
six                                1.15.0
sniffio                            1.2.0
snowballstemmer                    2.1.0
sortedcollections                  2.1.0
sortedcontainers                   2.3.0
soupsieve                          2.2.1
Sphinx                             4.0.1
sphinxcontrib-applehelp            1.0.2
sphinxcontrib-devhelp              1.0.2
sphinxcontrib-htmlhelp             1.0.3
sphinxcontrib-jsmath               1.0.1
sphinxcontrib-qthelp               1.0.3
sphinxcontrib-serializinghtml      1.1.4
sphinxcontrib-websupport           1.2.4
spyder                             4.2.5
spyder-kernels                     1.10.2
SQLAlchemy                         1.4.7
statsmodels                        0.12.2
sympy                              1.8
tables                             3.6.1
tblib                              1.7.0
terminado                          0.9.4
testpath                           0.4.4
textdistance                       4.2.1
threadpoolctl                      2.1.0
three-merge                        0.1.1
tifffile                           2021.4.8
toml                               0.10.2
toolz                              0.11.1
tornado                            6.1
tqdm                               4.59.0
traitlets                          5.0.5
typed-ast                          1.4.2
typing-extensions                  3.7.4.3
ujson                              4.0.2
unicodecsv                         0.14.1
urllib3                            1.26.4
watchdog                           1.0.2
wcwidth                            0.2.5
webencodings                       0.5.1
Werkzeug                           1.0.1
wheel                              0.36.2
widgetsnbextension                 3.5.1
win-inet-pton                      1.1.0
win-unicode-console                0.5
wincertstore                       0.2
wrapt                              1.12.1
xlrd                               2.0.1
XlsxWriter                         1.3.8
xlwings                            0.23.0
xlwt                               1.3.0
xmltodict                          0.12.0
yapf                               0.31.0
zict                               2.0.0
zipp                               3.4.1
zope.event                         4.5.0
zope.interface                     5.3.0
插入一个之后会接触到的小知识
有监督学习:其中数据带有一个附加属性,即我们想要预测的结果值
  分类:样本属于两个或更多个类,我们想从已经标记的数据中学习如何预测未标记数据的类别。 
  分类问题的一个例子是手写数字识别,其目的是将每个输入向量分配给有限数目的离散类别之一。
  我们通常把分类视作监督学习的一个离散形式(区别于连续形式),从有限的类别中,给每个样本贴上正确的标签。
  回归:如果期望的输出由一个或多个连续变量组成,则该任务称为 回归 。 回归问题的一个例子是预测鲑鱼的长度是其年龄和体重的函数。

无监督学习:其中训练数据由没有任何相应目标值的一组输入向量x组成。
这种问题的目标可能是在数据中发现彼此类似的示例所聚成的组,这种问题称为 聚类 , 或者,确定输入空间内的数据分布,称为密度估计,
又或从高维数据投影数据空间缩小到二维或三维以进行可视化

2.sklearn的内置数据集

# 从sklearn.datasets中导入鸢尾花数据集(小规模数据集)
from sklearn.datasets import load_iris
def dataset_test():
    iris = load_iris()
    # 输出iris中的键
    for key in iris.keys():
        print(key)
# 可以发现iris中有五个键:data,target,target_names,DESCR,feature_names
    # 我们依次来看一下 以及运行结果
    print(iris['data']) # 等同于print(iris.data),data中存放的是数据集
    print(iris.data.shape) # 返回数据集的形状(150, 4),150行4列
   
    print(iris['target']) # 等同于print(iris.target),target中存放的是目标值

    print(iris['target_names']) # 等同于print(iris.target_names),target_names存放的是目标值的名称
   
    print(iris['DESCR'])    # 等同于print(iris.DESCR),DESCR则是对这个数据集的描述
  
    print(iris['feature_names']) # 等同于print(iris.feature_names),feature_names指的是特征的名称

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_03,第3张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sklearn_04,第4张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_05,第5张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_06,第6张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sphinx_07,第7张

#导入iris库
from sklearn.datasets import load_iris
#加载iris数据
iris = load_iris()
iris
{'data': array([[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]]),
 'target': array([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]),
 'frame': None,
 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
 'DESCR': '.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n                \n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...',
 'feature_names': ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 'filename': 'D:\Anaconda3\lib\site-packages\sklearn\datasets\data\iris.csv'}
#以字典的形式返回
iris.keys()
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])
#数据集数据集所在位置
iris.filename
'D:\Anaconda3\lib\site-packages\sklearn\datasets\data\iris.csv'
#对数据集的介绍
print(iris.DESCR)
.. _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%PLU@io.arc.nasa.gov)
    :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 ...
#目标变量名
iris.target_names
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
#列名称
iris.target.shape
(150,)
iris.target
array([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.data#等价于iris['data']
array([[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]])
iris.feature_names
['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']
iris['data'].shape
(150, 4)
iris
{'data': array([[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]]),
 'target': array([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]),
 'frame': None,
 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
 'DESCR': '.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n                \n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...',
 'feature_names': ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 'filename': 'D:\Anaconda3\lib\site-packages\sklearn\datasets\data\iris.csv'}
#转成数据框
import pandas as pd#导入库叫pandas以便于对数据集进行操作 具体看其他博文
ris_df = pd.DataFrame(iris.data,columns = iris.feature_names)
iris_df



sepal length (cm)

sepal width (cm)

petal length (cm)

petal width (cm)

0

5.1

3.5

1.4

0.2

1

4.9

3.0

1.4

0.2

2

4.7

3.2

1.3

0.2

3

4.6

3.1

1.5

0.2

4

5.0

3.6

1.4

0.2

...

...

...

...

...

145

6.7

3.0

5.2

2.3

146

6.3

2.5

5.0

1.9

147

6.5

3.0

5.2

2.0

148

6.2

3.4

5.4

2.3

149

5.9

3.0

5.1

1.8

150 rows × 4 columns

#增加数据框的列
iris_df['class'] = iris.target
iris_df



sepal length (cm)

sepal width (cm)

petal length (cm)

petal width (cm)

class

0

5.1

3.5

1.4

0.2

0

1

4.9

3.0

1.4

0.2

0

2

4.7

3.2

1.3

0.2

0

3

4.6

3.1

1.5

0.2

0

4

5.0

3.6

1.4

0.2

0

...

...

...

...

...

...

145

6.7

3.0

5.2

2.3

2

146

6.3

2.5

5.0

1.9

2

147

6.5

3.0

5.2

2.0

2

148

6.2

3.4

5.4

2.3

2

149

5.9

3.0

5.1

1.8

2

150 rows × 5 columns

#导入波士顿房价数据集
from sklearn import datasets
boston = datasets.load_boston()
boston
{'data': array([[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]]),
 'target': array([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]),
 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),
 'DESCR': ".. _boston_dataset:\n\nBoston house prices dataset\n---------------------------\n\n**Data Set Characteristics:**  \n\n    :Number of Instances: 506 \n\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n\n    :Attribute Information (in order):\n        - CRIM     per capita crime rate by town\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n        - INDUS    proportion of non-retail business acres per town\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n        - NOX      nitric oxides concentration (parts per 10 million)\n        - RM       average number of rooms per dwelling\n        - AGE      proportion of owner-occupied units built prior to 1940\n        - DIS      weighted distances to five Boston employment centres\n        - RAD      index of accessibility to radial highways\n        - TAX      full-value property-tax rate per ,000\n        - PTRATIO  pupil-teacher ratio by town\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n        - LSTAT    % lower status of the population\n        - MEDV     Median value of owner-occupied homes in 00's\n\n    :Missing Attribute Values: None\n\n    :Creator: Harrison, D. and Rubinfeld, D.L.\n\nThis is a copy of UCI ML housing dataset.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n\n\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\nprices and the demand for clean air', J. Environ. Economics & Management,\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\npages 244-261 of the latter.\n\nThe Boston house-price data has been used in many machine learning papers that address regression\nproblems.   \n     \n.. topic:: References\n\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n   - 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.\n",
 'filename': 'D:\Anaconda3\lib\site-packages\sklearn\datasets\data\boston_house_prices.csv'}

使用sklearn.datasets.load_digits即可加载相关数据集其参数包括:
return_X_y:若为True,则以( data, ta arget )形式返回 数据﹔默认为False,表示以字典形式返回数据全部信息(包括data和target ) ; 就是Y和X形式的意味
n_class :表示返回数据的类别数,如: n class=5,则返 回o到4的数据样本。 就是结果返回几种类

#导入波士顿房价数据集
from sklearn.datasets import load_digits
digits = load_digits(return_X_y=True,n_class=5)
digits

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sklearn_08,第8张

#导入波士顿房价数据集
from sklearn.datasets import load_digits
digits = load_digits(return_X_y=True)
digits

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sklearn_09,第9张

boston.keys()
dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])
boston.filename
'D:\Anaconda3\lib\site-packages\sklearn\datasets\data\boston_house_prices.csv'
print(boston.DESCR)
.. _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 ,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 00'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.
boston.target.shape
(506,)
#转成数据框
import pandas as pd
boston_df = pd.DataFrame(boston.data,columns = boston.feature_names)
boston_df.head(20)



CRIM

ZN

INDUS

CHAS

NOX

RM

AGE

DIS

RAD

TAX

PTRATIO

B

LSTAT

0

0.00632

18.0

2.31

0.0

0.538

6.575

65.2

4.0900

1.0

296.0

15.3

396.90

4.98

1

0.02731

0.0

7.07

0.0

0.469

6.421

78.9

4.9671

2.0

242.0

17.8

396.90

9.14

2

0.02729

0.0

7.07

0.0

0.469

7.185

61.1

4.9671

2.0

242.0

17.8

392.83

4.03

3

0.03237

0.0

2.18

0.0

0.458

6.998

45.8

6.0622

3.0

222.0

18.7

394.63

2.94

4

0.06905

0.0

2.18

0.0

0.458

7.147

54.2

6.0622

3.0

222.0

18.7

396.90

5.33

5

0.02985

0.0

2.18

0.0

0.458

6.430

58.7

6.0622

3.0

222.0

18.7

394.12

5.21

6

0.08829

12.5

7.87

0.0

0.524

6.012

66.6

5.5605

5.0

311.0

15.2

395.60

12.43

7

0.14455

12.5

7.87

0.0

0.524

6.172

96.1

5.9505

5.0

311.0

15.2

396.90

19.15

8

0.21124

12.5

7.87

0.0

0.524

5.631

100.0

6.0821

5.0

311.0

15.2

386.63

29.93

9

0.17004

12.5

7.87

0.0

0.524

6.004

85.9

6.5921

5.0

311.0

15.2

386.71

17.10

10

0.22489

12.5

7.87

0.0

0.524

6.377

94.3

6.3467

5.0

311.0

15.2

392.52

20.45

11

0.11747

12.5

7.87

0.0

0.524

6.009

82.9

6.2267

5.0

311.0

15.2

396.90

13.27

12

0.09378

12.5

7.87

0.0

0.524

5.889

39.0

5.4509

5.0

311.0

15.2

390.50

15.71

13

0.62976

0.0

8.14

0.0

0.538

5.949

61.8

4.7075

4.0

307.0

21.0

396.90

8.26

14

0.63796

0.0

8.14

0.0

0.538

6.096

84.5

4.4619

4.0

307.0

21.0

380.02

10.26

15

0.62739

0.0

8.14

0.0

0.538

5.834

56.5

4.4986

4.0

307.0

21.0

395.62

8.47

16

1.05393

0.0

8.14

0.0

0.538

5.935

29.3

4.4986

4.0

307.0

21.0

386.85

6.58

17

0.78420

0.0

8.14

0.0

0.538

5.990

81.7

4.2579

4.0

307.0

21.0

386.75

14.67

18

0.80271

0.0

8.14

0.0

0.538

5.456

36.6

3.7965

4.0

307.0

21.0

288.99

11.69

19

0.72580

0.0

8.14

0.0

0.538

5.727

69.5

3.7965

4.0

307.0

21.0

390.95

11.28

boston_df['class'] = boston.target
boston_df



CRIM

ZN

INDUS

CHAS

NOX

RM

AGE

DIS

RAD

TAX

PTRATIO

B

LSTAT

class

0

0.00632

18.0

2.31

0.0

0.538

6.575

65.2

4.0900

1.0

296.0

15.3

396.90

4.98

24.0

1

0.02731

0.0

7.07

0.0

0.469

6.421

78.9

4.9671

2.0

242.0

17.8

396.90

9.14

21.6

2

0.02729

0.0

7.07

0.0

0.469

7.185

61.1

4.9671

2.0

242.0

17.8

392.83

4.03

34.7

3

0.03237

0.0

2.18

0.0

0.458

6.998

45.8

6.0622

3.0

222.0

18.7

394.63

2.94

33.4

4

0.06905

0.0

2.18

0.0

0.458

7.147

54.2

6.0622

3.0

222.0

18.7

396.90

5.33

36.2

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

501

0.06263

0.0

11.93

0.0

0.573

6.593

69.1

2.4786

1.0

273.0

21.0

391.99

9.67

22.4

502

0.04527

0.0

11.93

0.0

0.573

6.120

76.7

2.2875

1.0

273.0

21.0

396.90

9.08

20.6

503

0.06076

0.0

11.93

0.0

0.573

6.976

91.0

2.1675

1.0

273.0

21.0

396.90

5.64

23.9

504

0.10959

0.0

11.93

0.0

0.573

6.794

89.3

2.3889

1.0

273.0

21.0

393.45

6.48

22.0

505

0.04741

0.0

11.93

0.0

0.573

6.030

80.8

2.5050

1.0

273.0

21.0

396.90

7.88

11.9

506 rows × 14 columns

#导入并加载手写数字数据集(手写数字数据集包括1797个数字数据,每个数字由8*8的矩阵组成)
from sklearn.datasets import load_digits
digit = load_digits()
digit
{'data': array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
        [ 0.,  0.,  0., ..., 10.,  0.,  0.],
        [ 0.,  0.,  0., ..., 16.,  9.,  0.],
        ...,
        [ 0.,  0.,  1., ...,  6.,  0.,  0.],
        [ 0.,  0.,  2., ..., 12.,  0.,  0.],
        [ 0.,  0., 10., ..., 12.,  1.,  0.]]),
 'target': array([0, 1, 2, ..., 8, 9, 8]),
 'frame': None,
 'feature_names': ['pixel_0_0',
  'pixel_0_1',
  'pixel_0_2',
  'pixel_0_3',
  'pixel_0_4',
  'pixel_0_5',
  'pixel_0_6',
  'pixel_0_7',
  'pixel_1_0',
  'pixel_1_1',
  'pixel_1_2',
  'pixel_1_3',
  'pixel_1_4',
  'pixel_1_5',
  'pixel_1_6',
  'pixel_1_7',
  'pixel_2_0',
  'pixel_2_1',
  'pixel_2_2',
  'pixel_2_3',
  'pixel_2_4',
  'pixel_2_5',
  'pixel_2_6',
  'pixel_2_7',
  'pixel_3_0',
  'pixel_3_1',
  'pixel_3_2',
  'pixel_3_3',
  'pixel_3_4',
  'pixel_3_5',
  'pixel_3_6',
  'pixel_3_7',
  'pixel_4_0',
  'pixel_4_1',
  'pixel_4_2',
  'pixel_4_3',
  'pixel_4_4',
  'pixel_4_5',
  'pixel_4_6',
  'pixel_4_7',
  'pixel_5_0',
  'pixel_5_1',
  'pixel_5_2',
  'pixel_5_3',
  'pixel_5_4',
  'pixel_5_5',
  'pixel_5_6',
  'pixel_5_7',
  'pixel_6_0',
  'pixel_6_1',
  'pixel_6_2',
  'pixel_6_3',
  'pixel_6_4',
  'pixel_6_5',
  'pixel_6_6',
  'pixel_6_7',
  'pixel_7_0',
  'pixel_7_1',
  'pixel_7_2',
  'pixel_7_3',
  'pixel_7_4',
  'pixel_7_5',
  'pixel_7_6',
  'pixel_7_7'],
 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],
         [ 0.,  0., 13., ..., 15.,  5.,  0.],
         [ 0.,  3., 15., ..., 11.,  8.,  0.],
         ...,
         [ 0.,  4., 11., ..., 12.,  7.,  0.],
         [ 0.,  2., 14., ..., 12.,  0.,  0.],
         [ 0.,  0.,  6., ...,  0.,  0.,  0.]],
 
        [[ 0.,  0.,  0., ...,  5.,  0.,  0.],
         [ 0.,  0.,  0., ...,  9.,  0.,  0.],
         [ 0.,  0.,  3., ...,  6.,  0.,  0.],
         ...,
         [ 0.,  0.,  1., ...,  6.,  0.,  0.],
         [ 0.,  0.,  1., ...,  6.,  0.,  0.],
         [ 0.,  0.,  0., ..., 10.,  0.,  0.]],
 
        [[ 0.,  0.,  0., ..., 12.,  0.,  0.],
         [ 0.,  0.,  3., ..., 14.,  0.,  0.],
         [ 0.,  0.,  8., ..., 16.,  0.,  0.],
         ...,
         [ 0.,  9., 16., ...,  0.,  0.,  0.],
         [ 0.,  3., 13., ..., 11.,  5.,  0.],
         [ 0.,  0.,  0., ..., 16.,  9.,  0.]],
 
        ...,
 
        [[ 0.,  0.,  1., ...,  1.,  0.,  0.],
         [ 0.,  0., 13., ...,  2.,  1.,  0.],
         [ 0.,  0., 16., ..., 16.,  5.,  0.],
         ...,
         [ 0.,  0., 16., ..., 15.,  0.,  0.],
         [ 0.,  0., 15., ..., 16.,  0.,  0.],
         [ 0.,  0.,  2., ...,  6.,  0.,  0.]],
 
        [[ 0.,  0.,  2., ...,  0.,  0.,  0.],
         [ 0.,  0., 14., ..., 15.,  1.,  0.],
         [ 0.,  4., 16., ..., 16.,  7.,  0.],
         ...,
         [ 0.,  0.,  0., ..., 16.,  2.,  0.],
         [ 0.,  0.,  4., ..., 16.,  2.,  0.],
         [ 0.,  0.,  5., ..., 12.,  0.,  0.]],
 
        [[ 0.,  0., 10., ...,  1.,  0.,  0.],
         [ 0.,  2., 16., ...,  1.,  0.,  0.],
         [ 0.,  0., 15., ..., 15.,  0.,  0.],
         ...,
         [ 0.,  4., 16., ..., 16.,  6.,  0.],
         [ 0.,  8., 16., ..., 16.,  8.,  0.],
         [ 0.,  1.,  8., ..., 12.,  1.,  0.]]]),
 'DESCR': ".. _digits_dataset:\n\nOptical recognition of handwritten digits dataset\n--------------------------------------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 1797\n    :Number of Attributes: 64\n    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n    :Missing Attribute Values: None\n    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n    :Date: July; 1998\n\nThis is a copy of the test set of the UCI ML hand-written digits datasets\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n\nThe data set contains images of hand-written digits: 10 classes where\neach class refers to a digit.\n\nPreprocessing programs made available by NIST were used to extract\nnormalized bitmaps of handwritten digits from a preprinted form. From a\ntotal of 43 people, 30 contributed to the training set and different 13\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n4x4 and the number of on pixels are counted in each block. This generates\nan input matrix of 8x8 where each element is an integer in the range\n0..16. This reduces dimensionality and gives invariance to small\ndistortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n1994.\n\n.. topic:: References\n\n  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n    Graduate Studies in Science and Engineering, Bogazici University.\n  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n    Linear dimensionalityreduction using relevance weighted LDA. School of\n    Electrical and Electronic Engineering Nanyang Technological University.\n    2005.\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\n    Algorithm. NIPS. 2000.\n"}
digit.keys
<function Bunch.keys>
digit.data.shape
(1797, 64)
digit.images.shape
(1797, 8, 8)
digit.data
array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ..., 10.,  0.,  0.],
       [ 0.,  0.,  0., ..., 16.,  9.,  0.],
       ...,
       [ 0.,  0.,  1., ...,  6.,  0.,  0.],
       [ 0.,  0.,  2., ..., 12.,  0.,  0.],
       [ 0.,  0., 10., ..., 12.,  1.,  0.]])
#此点知识涉及matplotli,详情见其他博客
import matplotlib.pyplot as plt
%matplotlib inline
plt.matshow(digit.images[0])

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_10,第10张

sklearn的基本操作

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_11,第11张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_人工智能_12,第12张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_13,第13张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sphinx_14,第14张

模型选择 Model selection
参数和模型的比较、验证和选择。
目标:通过参数调整提高精度
模块:网格搜索,交叉验证,度量。
.
.
预处理Preprocessing
特征提取与归一化
应用:转换输入数据,如图文,用于机器学习算法。
模块:预处理,特征提取。

.
.

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sphinx_15,第15张

#sklearn"三板斧"
#实例化
#fit训练
#tranform转化orpredict预测

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_人工智能_16,第16张

1.实例化举例

from sklearn import preprocessing
std = preprocessing.StandardScaler()
std

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_人工智能_17,第17张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_18,第18张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sklearn_19,第19张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_20,第20张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_21,第21张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sphinx_22,第22张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_23,第23张

from sklearn import linear_model
reg = linear_model.LinearRegression()
reg

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_24,第24张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sklearn_25,第25张

1.实例化举例

from sklearn import decomposition
dec = decomposition.PCA()
dec

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_ci_26,第26张

from sklearn import svm
svv = svm.sVC()
svv

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scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_28,第28张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sphinx_29,第29张

2.fit举例

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_人工智能_30,第30张

3.fit_transform举例

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sklearn_31,第31张

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_sphinx_32,第32张

3.fit_predict举例

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模型的保存

scipy版本和python版本之间的关系 scipy和sklearn,scipy版本和python版本之间的关系 scipy和sklearn_python_34,第34张



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