Optimization of Decision Tree Machine Learning Strategy in Data Analysis

Abstract

Aiming at the problem of too coarse matching of machine learning decision tree models in the field of data mining and low prediction accuracy, the corresponding improved optimization strategies are proposed. First, the field matching degree of data is further improved by discretizing continuous attributes in multiple intervals. Then, the method makes the selection of business attributes more reasonable in the downward splitting process of the model by compensating the weight of feature attributes by business sensitivity indicators. Finally, the data classification rule transformation is used to further improve the data prediction accuracy of the model. The experimental results show that the introduction of the tree model generated by the business sensitivity index is more concise. In addition, the business pertinence and data classification capabilities are stronger. The results show that the transformed and upgraded data classification rules can effectively improve the accuracy of data prediction compared with the traditional optimization algorithm.