Research on IWOA-GBDT Regression Prediction Model Based on Gradient Lifting Decision Tree

This study developed and validated a regression prediction model based on improved whale optimization algorithm (IWOA) and gradient lifting decision tree (GBDT). This model shows high efficiency and accuracy in solving complex nonlinear relationship prediction problems, especially in data mining and machine learning applications. In this study, the GBDT model is debugged first, and then the IWOA algorithm is used to optimize the key parameters of GBDT by initializing the population through chaotic mapping sequence, adaptive inertia weighting and crossover mutation strategy, so as to find the optimal parameter combination to improve the prediction accuracy of the model, and the reliability of its prediction effect is verified by experimental results. The experimental results show that the IWOA-GBDT model is superior to the traditional GBDT model in many evaluation indexes, which shows that the IWOA algorithm effectively improves the prediction accuracy and reliability of the GBDT model. This study proves the effectiveness of IWOA-GBDT model in regression forecasting task. The model not only improves the prediction accuracy, but also shows good generalization ability. The successful application of this model provides a new perspective and method for future data mining and machine learning research.