交叉验证，五次五折，十次十折

交叉验证是一种评估机器学习模型的表现；

其中k折交叉验证就是指：将一个数据集分成k个部分，每次取其中的一份作为测试集，k-1份作为训练集；因此，k折交叉验证就会进行k次，获得k个结果后取平均值

以10折交叉验证为例：

图源周志华《机器学习》

底层实现代码：

for i in range(10):  # 十次
    data = readdataset("E:MLsmoteSynthetic_pima_naivesmote.xlsx")
    lines = data.shape[0]  # 行数
    test_ratio = 0.1  # 测试集比例
    t=data.shape[1] 
    label = data[:, t-1]
    test_line = int(lines * test_ratio)
    counts = 0
    for k in range(10):  # 十折
        for i in range(test_line):
            classifyresults = classify(data[i], data[test_line:lines], label[test_line:lines], 3)
            if classifyresults == label[i]:
                counts += 1
        aa = copy.deepcopy(data[0:test_line])
        data[0:lines - test_line] = data[test_line:lines]
        data[lines - test_line:lines] = aa
        #print(norm_data)
        bb = copy.deepcopy(label[0:test_line])
        label[0:lines - test_line] = label[test_line:lines]
        label[lines - test_line:lines] = bb

    print(knn10次10折交叉验证的正确率为{}.format(100*counts/lines))

也可以选择sklearn中stratifiedkFold分层采样函数，分层采样可以使得不平衡数据集中各个类别的平衡性，即不会出现测试集中没有少数类的现象

代码如下

from sklearn.model_selection import StratifiedKFold
id = 0
for i in range(5):
    skf = StratifiedKFold(n_splits=5, shuffle=True,random_state=None)
    id = 0
    for train_index,test_index in skf.split(X,y):
        id+=1
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = y[train_index], y[test_index]
        y_train = y_train[:, np.newaxis]
        y_test = y_test[:,np.newaxis]
        train = np.hstack((X_train,y_train))
        test = np.hstack((X_test,y_test))
        train = pd.DataFrame(train)
        test = pd.DataFrame(test)
        train.to_excel(E:\ML\smote\数据集划分\page-blocks-1-3_vs_4\+str(i+1)+\train+str(id)+.xlsx,index = False)
        test.to_excel(E:\ML\smote\数据集划分\page-blocks-1-3_vs_4\+str(i+1)+\test+str(id)+.xlsx,index = False)

交叉验证是一种评估机器学习模型的表现； 其中k折交叉验证就是指：将一个数据集分成k个部分，每次取其中的一份作为测试集，k-1份作为训练集；因此，k折交叉验证就会进行k次，获得k个结果后取平均值 以10折交叉验证为例： 图源周志华《机器学习》 底层实现代码： for i in range(10): # 十次 data = readdataset("E:MLsmoteSynthetic_pima_naivesmote.xlsx") lines = data.shape[0] # 行数 test_ratio = 0.1 # 测试集比例 t=data.shape[1] label = data[:, t-1] test_line = int(lines * test_ratio) counts = 0 for k in range(10): # 十折 for i in range(test_line): classifyresults = classify(data[i], data[test_line:lines], label[test_line:lines], 3) if classifyresults == label[i]: counts += 1 aa = copy.deepcopy(data[0:test_line]) data[0:lines - test_line] = data[test_line:lines] data[lines - test_line:lines] = aa #print(norm_data) bb = copy.deepcopy(label[0:test_line]) label[0:lines - test_line] = label[test_line:lines] label[lines - test_line:lines] = bb print(knn10次10折交叉验证的正确率为{}.format(100*counts/lines)) 也可以选择sklearn中stratifiedkFold分层采样函数，分层采样可以使得不平衡数据集中各个类别的平衡性，即不会出现测试集中没有少数类的现象 代码如下 from sklearn.model_selection import StratifiedKFold id = 0 for i in range(5): skf = StratifiedKFold(n_splits=5, shuffle=True,random_state=None) id = 0 for train_index,test_index in skf.split(X,y): id+=1 X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] y_train = y_train[:, np.newaxis] y_test = y_test[:,np.newaxis] train = np.hstack((X_train,y_train)) test = np.hstack((X_test,y_test)) train = pd.DataFrame(train) test = pd.DataFrame(test) train.to_excel(E:\ML\smote\数据集划分\page-blocks-1-3_vs_4\+str(i+1)+\train+str(id)+.xlsx,index = False) test.to_excel(E:\ML\smote\数据集划分\page-blocks-1-3_vs_4\+str(i+1)+\test+str(id)+.xlsx,index = False)