机器学习之路: python k近邻分类器 鸢尾花分类预测

  1 from sklearn.datasets import load_iris
  2 from sklearn.cross_validation import train_test_split
  3 from sklearn.preprocessing import StandardScaler
  4 from sklearn.neighbors import KNeighborsClassifier
  5 from sklearn.metrics import classification_report
  6 
  7 ‘‘‘
  8 k近邻分类器
  9 通过数据的分布对预测数据做出决策
 10 属于无参数估计的一种
 11 非常高的计算复杂度和内存消耗
 12 ‘‘‘
 13 
 14 ‘‘‘
 15 1 准备数据
 16 ‘‘‘
 17 # 读取鸢尾花数据集
 18 iris = load_iris()
 19 # 检查数据规模
 20 # print(iris.data.shape)    # (150, 4)
 21 # 查看数据说明
 22 # print(iris.DESCR)
 23 ‘‘‘
 24 Iris Plants Database
 25 ====================
 26 
 27 Notes
 28 -----
 29 Data Set Characteristics:
 30     :Number of Instances: 150 (50 in each of three classes)
 31     :Number of Attributes: 4 numeric, predictive attributes and the class
 32     :Attribute Information:
 33         - sepal length in cm
 34         - sepal width in cm
 35         - petal length in cm
 36         - petal width in cm
 37         - class:
 38                 - Iris-Setosa
 39                 - Iris-Versicolour
 40                 - Iris-Virginica
 41     :Summary Statistics:
 42 
 43     ============== ==== ==== ======= ===== ====================
 44                     Min  Max   Mean    SD   Class Correlation
 45     ============== ==== ==== ======= ===== ====================
 46     sepal length:   4.3  7.9   5.84   0.83    0.7826
 47     sepal width:    2.0  4.4   3.05   0.43   -0.4194
 48     petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
 49     petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
 50     ============== ==== ==== ======= ===== ====================
 51 
 52     :Missing Attribute Values: None
 53     :Class Distribution: 33.3% for each of 3 classes.
 54     :Creator: R.A. Fisher
 55     :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
 56     :Date: July, 1988
 57 
 58 This is a copy of UCI ML iris datasets.
 59 http://archive.ics.uci.edu/ml/datasets/Iris
 60 
 61 The famous Iris database, first used by Sir R.A Fisher
 62 
 63 This is perhaps the best known database to be found in the
 64 pattern recognition literature.  Fisher‘s paper is a classic in the field and
 65 is referenced frequently to this day.  (See Duda & Hart, for example.)  The
 66 data set contains 3 classes of 50 instances each, where each class refers to a
 67 type of iris plant.  One class is linearly separable from the other 2; the
 68 latter are NOT linearly separable from each other.
 69 
 70 References
 71 ----------
 72    - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
 73      Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
 74      Mathematical Statistics" (John Wiley, NY, 1950).
 75    - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
 76      (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
 77    - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
 78      Structure and Classification Rule for Recognition in Partially Exposed
 79      Environments".  IEEE Transactions on Pattern Analysis and Machine
 80      Intelligence, Vol. PAMI-2, No. 1, 67-71.
 81    - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
 82      on Information Theory, May 1972, 431-433.
 83    - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
 84      conceptual clustering system finds 3 classes in the data.
 85    - Many, many more ...
 86    
 87    共有150个数据样本
 88    均匀分布在3个亚种上
 89    每个样本采样4个花瓣、花萼的形状描述
 90 ‘‘‘
 91 
 92 ‘‘‘
 93 2 划分训练集合和测试集合
 94 ‘‘‘
 95 x_train, x_test, y_train, y_test = train_test_split(iris.data,
 96                                                     iris.target,
 97                                                     test_size=0.25,
 98                                                     random_state=33)
 99 
100 ‘‘‘
101 3 k近邻分类器 学习模型和预测
102 ‘‘‘
103 # 训练数据和测试数据进行标准化
104 ss = StandardScaler()
105 x_train = ss.fit_transform(x_train)
106 x_test = ss.transform(x_test)
107 
108 # 建立一个k近邻模型对象
109 knc = KNeighborsClassifier()
110 # 输入训练数据进行学习建模
111 knc.fit(x_train, y_train)
112 # 对测试数据进行预测
113 y_predict = knc.predict(x_test)
114 
115 ‘‘‘
116 4 模型评估
117 ‘‘‘
118 print("准确率：", knc.score(x_test, y_test))
119 print("其他指标:\n", classification_report(y_test, y_predict, target_names=iris.target_names))
120 ‘‘‘
121 准确率： 0.8947368421052632
122 其他指标:
123               precision    recall  f1-score   support
124 
125      setosa       1.00      1.00      1.00         8
126  versicolor       0.73      1.00      0.85        11
127   virginica       1.00      0.79      0.88        19
128 
129 avg / total       0.92      0.89      0.90        38
130 ‘‘‘