Application of Decision Tree Algorithm Based on Clustering and Entropy Method Level Division for Regional Economic Index Selection

The economy of a region is affected by many factors. The purpose of this study is to use the entropy method clustering and decision tree model fusion to find the main factors affecting the regional economy with the support of big data and empirical evidence. First extract some important indicators that affect the regional economy, and use the entropy method to find the relative weights and scores of these indicators. Then use K-means to divide these indicators into several intervals. Based on the entropy fusion model, obtain the ranking of each category of indicators, use these rankings as the objective value of the decision tree, and finally establish an economic indicator screening model. Participate in optimization and build a decision tree model that affects regional economic indicators. Through the visualization of the tree and the analysis of feature importance, you can intuitively see the main indicators that affect the regional economy, thereby achieving the research goals.