在KNN中，每个样本都可以被分类到一个特定的类标签中，其K个邻居所属的

In KNN, each sample can be classified into a specific class label, and the class labels to which its K neighbors account for the majority. The characteristic of the KNN classifier is its simple implementation. The essence of classification is to classify new samples that do not know the class label. The usual practice is to divide the data set into training and test data. Each sample in the test data needs to determine its K nearest neighbors from the training data. According to (22). Calculate the distance between the training group and the test group, defined as training and H test.

在KNN中，每个样本都可以被分类到一个特定的类标签中，其K个邻居所属的类标签占大多数。KNN分类器的特点是实现简单。分类的精髓是对不知道类别标签的新样本进行分类。通常的做法是将数据集分为训练和测试数据。测试数据中的每个样本需要从训练数据中确定其K个最近邻。根据（22）。计算训练组和测试组之间的距离，定义为训练和H检验。

In KNN, each sample can be classified into a specific class label, and the class labels of K neighbors account for the majority. KNN classifier is characterized by simple implementation. The essence of classification is to classify the new samples that do not know the category label. It is common practice to divide data sets into training and test data. Each sample in the test data needs to determine its K nearest neighbors from the training data. According to (22). Calculate the distance between training group and test group, defined as training and H-Test.