鸢尾花分类预测数据分析

Love The Way You Lie 2022-02-09 20:45 327阅读 0赞

## 鸢尾花分类预测数据分析 ##

*  **目标：根据未知种类鸢尾花的特征预测其种类**
 *  数据：鸢尾花数据集
 *  分析：  
    
    
     *  描述性分析
     *  探索性分析  
        
        
         *  建模分析
         *  模型分析
         *  迭代分析
 *  成果：位置种类鸢尾花的预测结果

import numpy as np
    import matplotlib.pyplot as plt
    # import pandas as pd
    from sklearn import neighbors, datasets

## 加载鸢尾花数据集 ##

iris = datasets.load_iris()
    iris

{'DESCR': 'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\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    :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\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\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\nReferences\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 ...\n',
     '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,  0.1],
            [ 5. ,  3.2,  1.2,  0.2],
            [ 5.5,  3.5,  1.3,  0.2],
            [ 4.9,  3.1,  1.5,  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]]),
     'feature_names': ['sepal length (cm)',
      'sepal width (cm)',
      'petal length (cm)',
      'petal width (cm)'],
     '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]),
     'target_names': array(['setosa', 'versicolor', 'virginica'], 
           dtype='<U10')}

## 数据认知 ##

type(iris)

sklearn.datasets.base.Bunch

数据集，样本150个

150行，4列

'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],
      ...
    ])

--------------------

特征：4个

'feature_names': [
      'sepal length (cm)', #花萼长度
      'sepal width (cm)', #花萼宽度
      'petal length (cm)', #花瓣长度
      'petal width (cm)' #花瓣宽度
    ],

--------------------

结果

标签1个：1行，150列

'target': array([
      0, 0, 0... 1, 1, 1... 2, 2, 2...
    ])

--------------------

结果对应

'target_names': array([
      'setosa', # 0 山鸢尾
      'versicolor', # 1 变色鸢尾
      'virginica' # 2 维吉尼亚鸢尾
    ]}

## 描述性分析 ##

se = iris.data[0:50] # 山鸢尾特征，50行
    ve = iris.data[50:100] # 变色鸢尾特征，50行
    vi = iris.data[100:150] # 维吉尼亚特征，50行
    se
    
    comb = [[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]] #二维图像，4个特征两两组合
    
    se[:,comb[0][0]] # 山鸢尾特征1，花萼长

array([ 5.1,  4.9,  4.7,  4.6,  5. ,  5.4,  4.6,  5. ,  4.4,  4.9,  5.4,
            4.8,  4.8,  4.3,  5.8,  5.7,  5.4,  5.1,  5.7,  5.1,  5.4,  5.1,
            4.6,  5.1,  4.8,  5. ,  5. ,  5.2,  5.2,  4.7,  4.8,  5.4,  5.2,
            5.5,  4.9,  5. ,  5.5,  4.9,  4.4,  5.1,  5. ,  4.5,  4.4,  5. ,
            5.1,  4.8,  5.1,  4.6,  5.3,  5. ])

## 图像绘制 ##

plt.figure(1, figsize=(18,10)) 
    
    for i in range(6):
        plt.subplot(231+i)
        plt.plot(se[:,comb[i][0]],se[:,comb[i][1]],'o',color='#ff0000')
        plt.plot(ve[:,comb[i][0]],ve[:,comb[i][1]],'^',color='#00ff00')
        plt.plot(vi[:,comb[i][0]],vi[:,comb[i][1]],'+',color='#ff00ff')
    
    plt.show()

![这里写图片描述][SouthEast]

## 探索性分析 ##

#### 建模分析 ####

# x轴，训练数据
    x = iris.data
    x

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,  0.1],
           [ 5. ,  3.2,  1.2,  0.2],
           [ 5.5,  3.5,  1.3,  0.2],
           [ 4.9,  3.1,  1.5,  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]])

# y轴，标签，训练结果
    y = iris.target
    y

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])

## 模型训练 ##

# knn训练
    x = iris.data[:, 0:2] # 特征1，2
    x
    y = iris.target
    y
    
    clf = neighbors.KNeighborsClassifier(n_neighbors = 15)
    clf.fit(x, y) # 模型训练
    clf

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
               metric_params=None, n_jobs=1, n_neighbors=15, p=2,
               weights='uniform')

# knn预测
    z = clf.predict(iris.data[:, 0:2]) # 特征1，2
    z

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, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2,
           1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 1, 1, 1,
           2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,
           1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1])

## 模型验证，正确率 ##

# 预测正确率
    correct = 0
    for i in range(len(iris.data)):
        if z[i] == iris.target[i]:
            correct += 1
    correct
    correct/len(iris.data)

0.8066666666666666

## 迭代优化 ##

# knn训练
    x = np.c_[iris.data[:, 2], iris.data[:, 3]] # 特征3，4
    y = iris.target
    
    clf = neighbors.KNeighborsClassifier(n_neighbors = 15)
    clf.fit(x,y)
    clf

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
               metric_params=None, n_jobs=1, n_neighbors=15, p=2,
               weights='uniform')

#knn预测
    
    z = clf.predict(np.c_[iris.data[:,2],iris.data[:,3]]) #特征3，4
    z

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, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
           2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,
           2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

#预测正确率
    
    correct = 0
    for i in range(len(iris.data)):
        if z[i] == iris.target[i]:
            correct += 1
    
    correct/len(iris.data)

0.96

机器学习过程中  
\* 特征最重要  
\* 机器学习算法，次要（信息熵）  
\* 热力学熵，是衡量物质混乱程度的一种度量  
\* 信息学熵，衡量信息大小的一种度量（出人意料，与众不同），香农

[SouthEast]: /images/20220210/b520bdd7613a441eaf5abf38ef88d7fb.png