In one-to-many model training, one category is used as a positive example, and the remaining examples are used as negative examples to train K models; when making predictions, if one of the K models is output as a positive example, then the final The prediction result belongs to this category of the classifier; if multiple positive examples are generated, the confidence of the classifier can be selected as the indicator, and the classifier with the highest confidence is selected as the final prediction result. Common confidences are accuracy score and recall rate.