机器学习之路: python k近邻分类器 KNeighborsClassifier 鸢尾花分类预测

使用python语言 学习k近邻分类器的api

欢迎来到我的git查看源代码: https://github.com/linyi0604/MachineLearning

 from sklearn.datasets import load_iris
 from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.metrics import classification_report

 '''
 k近邻分类器
 通过数据的分布对预测数据做出决策
 属于无参数估计的一种
 非常高的计算复杂度和内存消耗
 '''

 '''
 1 准备数据
 '''
 # 读取鸢尾花数据集
 iris = load_iris()
 # 检查数据规模
 # print(iris.data.shape)    # (150, 4)
 # 查看数据说明
 # print(iris.DESCR)
 '''
 Iris Plants Database
 ====================

 Notes
 -----
 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

 This is a copy of UCI ML iris datasets.
 http://archive.ics.uci.edu/ml/datasets/Iris

 The famous Iris database, first used by Sir R.A Fisher

 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.

 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 ...

    共有150个数据样本
    均匀分布在3个亚种上
    每个样本采样4个花瓣、花萼的形状描述
 '''

 '''
 2 划分训练集合和测试集合
 '''
 x_train, x_test, y_train, y_test = train_test_split(iris.data,
                                                     iris.target,
                                                     test_size=0.25,
                                                     random_state=33)

 '''
 3 k近邻分类器 学习模型和预测
 '''
 # 训练数据和测试数据进行标准化
 ss = StandardScaler()
 x_train = ss.fit_transform(x_train)
 x_test = ss.transform(x_test)

 # 建立一个k近邻模型对象
 knc = KNeighborsClassifier()
 # 输入训练数据进行学习建模
 knc.fit(x_train, y_train)
 # 对测试数据进行预测
 y_predict = knc.predict(x_test)

 '''
 4 模型评估
 '''
 print("准确率：", knc.score(x_test, y_test))
 print("其他指标:\n", classification_report(y_test, y_predict, target_names=iris.target_names))
 '''
 准确率： 0.8947368421052632
 其他指标:
               precision    recall  f1-score   support

      setosa       1.00      1.00      1.00         8
  versicolor       0.73      1.00      0.85        11
   virginica       1.00      0.79      0.88        19

 avg / total       0.92      0.89      0.90        38
 '''