Analysis of an Economic Coupling Relationship Model of the Coastal Ecological Fragile Zone Based on a Machine Learning Model

Abstract

This study is based on a decision tree learning model and integrated system. In order to improve the classification accuracy of the base learner, the diversity of the results is analyzed. Accuracy and diversity are the focus of decision tree classifier research. The balance between decision tree classifiers is a key issue in creating a good ensemble classifier. By reducing the redundant features and training the model with the optimal feature subset, the accuracy of the model can be improved and the running time can be reduced. Normal distribution and generalized error distribution are better. The clustering algorithm based on the K-means partition is a number of compact and independent classification clusters based on the K-means partition algorithm. In order to objectively and accurately treat the machine learning model, an analysis model and economic coupling relationship model were used to evaluate the coastal ecological fragile zone. The error of the result of the classifier after training is smaller.