机器学习入门(一)

目录

前言

一、机器学习概述

1. 什么是机器学习

2. 机器学习问题分类

2.1 监督学习

2.2 半监督学习

2.3 无监督学习

2.4 强化学习

3. 机器学习开发流程

二、初步使用数据集

1. 鸢尾花数据集的获取

2. 鸢尾花数据集的划分

2.1 说明

2.2 划分鸢尾花数据集代码

总结


前言

本文是在入手机器学习过程中的一些学习心得和总结,适合机器学习的基础阶段借鉴。


一、机器学习概述

1. 什么是机器学习

        机器学习是一种从数据中总结规律的统计方法。机器学习中有各种用于总结规律并且进行预测或者分类的模型(算法),被广泛应用在物体识别、语音识别、放假预测和疾病诊断等领域。

2. 机器学习问题分类

        机器学习问题可以分为监督学习问题、半监督学习问题和无监督学习问题三类,当然也可以分为有监督学习问题、无监督学习问题和强化学习问题。

2.1 监督学习

        输入数据是由特征值和目标值所组成,输出数据可以是连续型的数值,也可以是离散型的数值;如果输出数据是预测到的连续型数值,通常称为回归问题,反之,则通常称为分类问题。一般的分类问题有二分类问题(例如:是与否的问题)和多分类问题(例如:一篇文章属于哪种类型)。即监督学习要求对于输入有相应的输出。

        对于监督学习常用的算法有:k-近邻算法、贝叶斯分类、决策树与随机森林、逻辑回归(回归可分为线性回归和岭回归)。

2.2 半监督学习

        半监督学习和监督学习类似,有输入有输出,但是对于半监督学习而言,目标值与监督学习相比较少。例如有一个猫和狗的模型,对于半监督学习来说,有可能因为猫具有和狗相似的特征,但是由于目标值较少,从而造成误判,即将具有和狗有相似特征的猫判定为狗。和监督学习类似,半监督学习也要求有输入和输出。

2.3 无监督学习

        输入数据全部由特征值组成,而没有目标值,主要用来发现输入数据的规律。常见的问题是聚类问题和生成问题;聚类问题可以理解为如何将聚在一起的数据进行分类、生成问题可以理解为淘宝根据浏览的商品自动推送其他类似的商品。

2.4 强化学习

        强化学习可以理解为像下围棋,通过一定手段得出最优解。

3. 机器学习开发流程

        (1)获取数据

        (2)数据处理

        (3)特征工程

        (4)使用机器学习算法训练形成模型

        (5)模型评估

        (6)应用

二、初步使用数据集

1. 鸢尾花数据集的获取

        学习阶段数据集的获取可以通过sklearn、kaggle和UCL进行获取,本文的数据集均是从skearn进行获取。

        获取鸢尾花数据集的代码如下:

# 主 题:访问鸢尾花数据集
# 开发者:赵尘临
# 开发时间:22年6月25日
from sklearn.datasets import load_iris  # 导入sklearn.datasets模块中的load_iris类
print('---------------------------获取鸢尾花的数据集-----------------------------')
iris = load_iris()  # 将鸢尾花数据集类实例化为对象
print(type(iris))
print('鸢尾花数据集:n', iris)
print('鸢尾花数据集的描述:n', iris.DESCR)  # 访问数据集的描述
print('----------------------将鸢尾花数据集的数据打印出来--------------------------')
'''
--由于鸢尾花数据集实例化后形成的返回值是一个继承自字典的Bench,所以可以用字典的方法获取相应的数据--
'''
data = iris.data  # 使用属性的引用方法获取数据集的数据
print('鸢尾花特征值数据:n', data)
print('鸢尾花特征值数据:n', iris['data'])  # 使用获取字典键值方法获取数据集的数据
print('鸢尾花数据的shape值:n', data.shape)  # 访问矩阵类成员变量的shape值
print('鸢尾花数据集的特征值名称:n', iris.feature_names)  # 访问特征值的名称
print('鸢尾花数据集的标签名称:n', iris.target_names)  # 访问标签的名称
print(iris.target[[10, 25, 50]])  # 由于数据集的标签值一共有150个样本,相当于一个一维数组,选择第10、25、50个数据,将其对应的标签打印出来

        执行结果如下:

---------------------------获取鸢尾花的数据集-----------------------------
<class 'sklearn.utils._bunch.Bunch'>
鸢尾花数据集:
 {'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:nnIris plants datasetn--------------------nn**Data Set Characteristics:**nn    :Number of Instances: 150 (50 in each of three classes)n    :Number of Attributes: 4 numeric, predictive attributes and the classn    :Attribute Information:n        - sepal length in cmn        - sepal width in cmn        - petal length in cmn        - petal width in cmn        - class:n                - Iris-Setosan                - Iris-Versicolourn                - Iris-Virginican                n    :Summary Statistics:nn    ============== ==== ==== ======= ===== ====================n                    Min  Max   Mean    SD   Class Correlationn    ============== ==== ==== ======= ===== ====================n    sepal length:   4.3  7.9   5.84   0.83    0.7826n    sepal width:    2.0  4.4   3.05   0.43   -0.4194n    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    ============== ==== ==== ======= ===== ====================nn    :Missing Attribute Values: Nonen    :Class Distribution: 33.3% for each of 3 classes.n    :Creator: R.A. Fishern    :Donor: Michael Marshall (MARSHALL%[email protected])n    :Date: July, 1988nnThe famous Iris database, first used by Sir R.A. Fisher. The dataset is takennfrom Fisher's paper. Note that it's the same as in R, but not as in the UCInMachine Learning Repository, which has two wrong data points.nnThis is perhaps the best known database to be found in thenpattern recognition literature.  Fisher's paper is a classic in the field andnis referenced frequently to this day.  (See Duda & Hart, for example.)  Thendata set contains 3 classes of 50 instances each, where each class refers to antype of iris plant.  One class is linearly separable from the other 2; thenlatter are NOT linearly separable from each other.nn.. topic:: Referencesnn   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions ton     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 Systemn     Structure and Classification Rule for Recognition in Partially Exposedn     Environments".  IEEE Transactions on Pattern Analysis and Machinen     Intelligence, Vol. PAMI-2, No. 1, 67-71.n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactionsn     on Information Theory, May 1972, 431-433.n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS IIn     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': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
鸢尾花数据集的描述:
 .. _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]o.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 ...
----------------------将鸢尾花数据集的数据打印出来--------------------------
鸢尾花特征值数据:
 [[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]]
鸢尾花特征值数据:
 [[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]]
鸢尾花数据的shape值:
 (150, 4)
鸢尾花数据集的特征值名称:
 ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
鸢尾花数据集的标签名称:
 ['setosa' 'versicolor' 'virginica']
[0 0 1]

Process finished with exit code 0

2. 鸢尾花数据集的划分

2.1 说明

        一般情况下可以将数据集划分为训练数据集和测试数据集,这是因为在形成使用机器学习算法训练生成模型的时候需要进行模型评估,这个时候就需要测试数据集,所以不能将所有数据都用来训练模型,而是要留一部分数据进行测试。

2.2 划分鸢尾花数据集代码

        代码如下:

# 主 题:鸢尾花数据集的划分
# 开发者:赵尘临
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
iris = load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2,
                                                        random_state=22)  # test_size默认0.25
print('训练集的特征值:n', x_train, x_train.shape)
print('训练集的目标值:n', y_train, y_train.shape)
print('测试集的特征值:n', x_test, x_test.shape)
print('测试集的目标值:n', y_test, y_test.shape)

注意:train_test_split实例化为对象时的返回值顺序,规定为训练集特征值、测试集特征值、训练集目标值、测试集目标值,顺序不能改变。

        运行结果如下:

F:Python3.10.2python.exe F:/Pycharm2021.1.3/Project/Machine_learning/practice_1.py
训练集的特征值:
 [[4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [5.7 2.8 4.5 1.3]
 [5.  3.4 1.6 0.4]
 [5.1 3.4 1.5 0.2]
 [4.9 3.6 1.4 0.1]
 [6.9 3.1 5.4 2.1]
 [6.7 2.5 5.8 1.8]
 [7.  3.2 4.7 1.4]
 [6.3 3.3 4.7 1.6]
 [5.4 3.9 1.3 0.4]
 [4.4 3.2 1.3 0.2]
 [6.7 3.  5.  1.7]
 [5.6 3.  4.1 1.3]
 [5.7 2.5 5.  2. ]
 [6.5 3.  5.8 2.2]
 [5.  3.6 1.4 0.2]
 [6.1 2.8 4.  1.3]
 [6.  3.4 4.5 1.6]
 [6.7 3.  5.2 2.3]
 [5.7 4.4 1.5 0.4]
 [5.4 3.4 1.7 0.2]
 [5.  3.5 1.3 0.3]
 [4.8 3.  1.4 0.1]
 [5.5 4.2 1.4 0.2]
 [4.6 3.6 1.  0.2]
 [7.2 3.2 6.  1.8]
 [5.1 2.5 3.  1.1]
 [6.4 3.2 4.5 1.5]
 [7.3 2.9 6.3 1.8]
 [4.5 2.3 1.3 0.3]
 [5.  3.  1.6 0.2]
 [5.7 3.8 1.7 0.3]
 [5.  3.3 1.4 0.2]
 [6.2 2.2 4.5 1.5]
 [5.1 3.5 1.4 0.2]
 [6.4 2.9 4.3 1.3]
 [4.9 2.4 3.3 1. ]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [5.9 3.2 4.8 1.8]
 [5.4 3.9 1.7 0.4]
 [6.  2.2 4.  1. ]
 [6.4 2.8 5.6 2.1]
 [4.8 3.4 1.9 0.2]
 [6.4 3.1 5.5 1.8]
 [5.9 3.  4.2 1.5]
 [6.5 3.  5.5 1.8]
 [6.  2.9 4.5 1.5]
 [5.5 2.4 3.8 1.1]
 [6.2 2.9 4.3 1.3]
 [5.2 4.1 1.5 0.1]
 [5.2 3.4 1.4 0.2]
 [7.7 2.6 6.9 2.3]
 [5.7 2.6 3.5 1. ]
 [4.6 3.4 1.4 0.3]
 [5.8 2.7 4.1 1. ]
 [5.8 2.7 3.9 1.2]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]
 [4.6 3.1 1.5 0.2]
 [5.8 2.8 5.1 2.4]
 [5.1 3.5 1.4 0.3]
 [6.8 3.2 5.9 2.3]
 [4.9 3.1 1.5 0.1]
 [5.5 2.3 4.  1.3]
 [5.1 3.7 1.5 0.4]
 [5.8 2.7 5.1 1.9]
 [6.7 3.1 4.4 1.4]
 [6.8 3.  5.5 2.1]
 [5.2 2.7 3.9 1.4]
 [6.7 3.1 5.6 2.4]
 [5.3 3.7 1.5 0.2]
 [5.  2.  3.5 1. ]
 [6.6 2.9 4.6 1.3]
 [6.  2.7 5.1 1.6]
 [6.3 2.3 4.4 1.3]
 [7.7 3.  6.1 2.3]
 [4.9 3.  1.4 0.2]
 [4.6 3.2 1.4 0.2]
 [6.3 2.7 4.9 1.8]
 [6.6 3.  4.4 1.4]
 [6.9 3.1 4.9 1.5]
 [4.3 3.  1.1 0.1]
 [5.6 2.7 4.2 1.3]
 [4.8 3.4 1.6 0.2]
 [7.6 3.  6.6 2.1]
 [7.7 2.8 6.7 2. ]
 [4.9 2.5 4.5 1.7]
 [6.5 3.2 5.1 2. ]
 [5.1 3.3 1.7 0.5]
 [6.3 2.9 5.6 1.8]
 [6.1 2.6 5.6 1.4]
 [5.  3.4 1.5 0.2]
 [6.1 3.  4.6 1.4]
 [5.6 3.  4.5 1.5]
 [5.1 3.8 1.5 0.3]
 [5.6 2.8 4.9 2. ]
 [4.4 3.  1.3 0.2]
 [5.5 2.4 3.7 1. ]
 [4.7 3.2 1.6 0.2]
 [6.7 3.3 5.7 2.5]
 [5.2 3.5 1.5 0.2]
 [6.4 2.7 5.3 1.9]
 [6.3 2.8 5.1 1.5]
 [4.4 2.9 1.4 0.2]
 [6.1 3.  4.9 1.8]
 [4.9 3.1 1.5 0.2]
 [5.  2.3 3.3 1. ]
 [4.8 3.  1.4 0.3]
 [5.8 4.  1.2 0.2]
 [6.3 3.4 5.6 2.4]
 [5.4 3.  4.5 1.5]
 [7.1 3.  5.9 2.1]
 [6.3 3.3 6.  2.5]
 [5.1 3.8 1.9 0.4]
 [6.4 2.8 5.6 2.2]
 [7.7 3.8 6.7 2.2]] (120, 4)
训练集的目标值:
 [0 0 1 1 1 0 0 0 2 2 1 1 0 0 1 1 2 2 0 1 1 2 0 0 0 0 0 0 2 1 1 2 0 0 0 0 1
 0 1 1 1 1 1 0 1 2 0 2 1 2 1 1 1 0 0 2 1 0 1 1 2 2 0 2 0 2 0 1 0 2 1 2 1 2
 0 1 1 1 1 2 0 0 2 1 1 0 1 0 2 2 2 2 0 2 2 0 1 1 0 2 0 1 0 2 0 2 2 0 2 0 1
 0 0 2 1 2 2 0 2 2] (120,)
测试集的特征值:
 [[5.4 3.7 1.5 0.2]
 [6.4 3.2 5.3 2.3]
 [6.5 2.8 4.6 1.5]
 [6.3 2.5 5.  1.9]
 [6.1 2.9 4.7 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.1 4.7 1.5]
 [6.  3.  4.8 1.8]
 [5.6 2.9 3.6 1.3]
 [5.  3.2 1.2 0.2]
 [6.9 3.2 5.7 2.3]
 [5.7 3.  4.2 1.2]
 [7.4 2.8 6.1 1.9]
 [7.2 3.6 6.1 2.5]
 [5.  3.5 1.6 0.6]
 [7.9 3.8 6.4 2. ]
 [5.6 2.5 3.9 1.1]
 [5.7 2.8 4.1 1.3]
 [6.  2.2 5.  1.5]
 [5.7 2.9 4.2 1.3]
 [5.1 3.8 1.6 0.2]
 [6.9 3.1 5.1 2.3]
 [5.5 3.5 1.3 0.2]
 [5.8 2.6 4.  1.2]
 [5.8 2.7 5.1 1.9]
 [4.7 3.2 1.3 0.2]
 [7.2 3.  5.8 1.6]
 [6.5 3.  5.2 2. ]
 [6.7 3.3 5.7 2.1]
 [6.2 2.8 4.8 1.8]] (30, 4)
测试集的目标值:
 [0 2 1 2 1 1 1 2 1 0 2 1 2 2 0 2 1 1 2 1 0 2 0 1 2 0 2 2 2 2] (30,)

Process finished with exit code 0

总结

本文对机器学习做了简单介绍,并且对常见的鸢尾花数据集做了数据获取和数据划分。

本图文内容来源于网友网络收集整理提供,作为学习参考使用,版权属于原作者。
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