第1关 使用sklearn中的kNN算法进行分类

from sklearn.neighbors import KNeighborsClassifier

def classification(train_feature, train_label, test_feature):
    '''
    使用KNeighborsClassifier对test_feature进行分类
    :param train_feature: 训练集数据
    :param train_label: 训练集标签
    :param test_feature: 测试集数据
    :return: 测试集预测结果
    '''

    #********* Begin *********#
    clf = KNeighborsClassifier()
    clf.fit(train_feature, train_label)
    return clf.predict(test_feature)
    #********* End *********#

第2关 使用sklearn中的kNN算法进行回归

from sklearn.neighbors import KNeighborsRegressor

def regression(train_feature, train_label, test_feature):
    '''
    使用KNeighborsRegressor对test_feature进行分类
    :param train_feature: 训练集数据
    :param train_label: 训练集标签
    :param test_feature: 测试集数据
    :return: 测试集预测结果
    '''

    #********* Begin *********#
    clf=KNeighborsRegressor() 
    clf.fit(train_feature, train_label)               
    return clf.predict(test_feature)
    #********* End *********#