機器學習之 KNN近鄰算法（二）鳶尾花數據集訓練

一、鳶尾花數據集

 from sklearn.datasets import load_iris ，通過 datas= load_iris() 獲得鳶尾花數據集用於測試

iris里有data和target等等

{'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], ... 省略 ... [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 ([e-mail])\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:\\PyProjects\\demo\\venv\\lib\\site-packages\\sklearn\\datasets\\data\\iris.csv'}

二、問題：如何建立最准確的模型？

Question1. 如何科學分配 (train,test) ，按照下標二八分有問題 

• Answer1-1: 打亂下標

 # 准備一個亂數的下標序列號
    index=np.arange(150); # 0~149 np.random.shuffle(index) # shuffle  # 根據給定的亂序的下標到對應的數組中提取對應的數據
    train,test=datas.data[index[:100]],datas.data[index[100:]] train_target,real_target=datas.target[index[:100]],datas.target[index[100:]]

Question2. 如何確定 n_neighbors ？

• Answer2-1:使用 交叉驗證方法

為什么要用？

1. 最重要：評估模型性能
2. Answer1-1的打亂下標方法可以再優化            =>    1）交叉驗證，相當於擴充了有限數據集；2）用不同數據集測試，可以說明有一定泛化能力 
3. 確定近鄰數n（通過尋找最高的准確率score）=>    求超參數

怎么做？

      （設cv=10）所有數據集分成10折，可以保證每折數據集都作為train（9次）和test（1次），最后取mean

相關文章鏈接1：https://blog.csdn.net/weixin_42211626/article/details/100064842

相關文章鏈接2：https://blog.csdn.net/qq_36523839/article/details/80707678

 from sklearn.model_selection import cross_val_score 

    k_range=range(1,31) k_score=[] for k in k_range: knn=KNeighborsClassifier(n_neighbors=k) # 模型評估數據(交叉評估)
    # from sklearn.model_selection import cross_val_score
        scores=cross_val_score(knn,train,target,cv=10,scoring='accuracy') k_score.append(scores.mean()) plt.plot(k_range,k_score,'r') 　　# import matplotlib.pyplot as plt
　　# plt.text：貼標簽
    for x1,y1 in zip(k_range,k_score): plt.text(x1,y1,str(x1), ha='center', va='bottom', fontsize=10, rotation=0) # 數據作圖
　　 plt.show()

分析：

三、結果