Big data modeling of product value-added services based on SAMME-CART algorithm

For the life cycle of a product, the after-sales value-added services of a product can bring huge value-added effects to the product value. The most important after-sales service is the maintenance of the product. It is extremely important to predict the fault type of product accurately and timely. Most of the existing fault type prediction models use a single algorithm, ignoring the limitations of a single algorithm. In this paper, a multi-classification model is established by using the SAMME algorithm and the decision tree constructed by the Classification and Decision Tree algorithm as the base classifier to predict the failure type of the ultrasonic flowmeter. In the ultrasonic flowmeter fault type prediction experiment, the results show that the multi-classification model constructed in this paper is better than the multi-classification model constructed by a single algorithm in accuracy and other indicators. So that it can provide a reference for people who carry out related work in this business domain.