Research on User Upgrade and Replacement Prediction Algorithm Based on Improved LightGBM Algorithm

In practical applications, the significance of accurately identifying potential car buyers far outweighs the risk of misclassifying non-buyers as potential buyers, thus the importance of the recall rate for positive samples. Consequently, this algorithm develops an enhanced incremental replacement prediction model designed to improve the recall rate of positive samples. Additionally, the algorithm introduces a novel approach for categorizing and labeling factors related to the age of replacement vehicles, thereby providing advanced data processing techniques and evaluation metrics to better describe the influence of various factors on the age of replacement vehicles. This methodology significantly enhances the accuracy of predicting the age of replacement vehicles.