Residual Value Prediction of New Energy Vehicles Based on Machine Learning Algorithms
TL;DR
The FVIM-XGBoost regression algorithm is proposed, which integrates the four-vector intelligent meta-heuristic optimization algorithm and the extreme gradient boosting model and performs better in regression prediction tasks and can provide precise and reliable technical support for the assessment of the residual value of new energy vehicles, pricing decisions, etc.
Abstract
Under the impetus of global energy transition and the promotion of the dual carbon goals, there is an urgent need for quantitative decision-making in the prediction of the residual value of new energy vehicles. Traditional assessment methods and conventional machine learning models have problems such as insufficient feature mining, limited prediction accuracy, and high risk of overfitting. This paper proposes the FVIM-XGBoost regression algorithm, which integrates the four-vector intelligent meta-heuristic optimization algorithm and the extreme gradient boosting model. Through the feature value optimization module, it screens and reconstructs features, filters out redundant noise, combines the regularization advantages of XGBoost and the gradient optimization advantages, and improves the model's fitting accuracy and generalization ability. The experiment is based on multi-source collected and standardized processed data of new energy vehicles. FVIM is used to optimize the key hyperparameters of XGBoost, and an efficient and robust prediction scheme is constructed. Compared with multiple mainstream machine learning models, this algorithm performs better in regression prediction tasks and can provide precise and reliable technical support for the assessment of the residual value of new energy vehicles, pricing decisions, etc.