Abstract

Decision tree algorithm is a widely used classification and prediction method. Because it generates a tree-like classifier, it has a simple structure and is extensively used by people. Regardless of the decision tree algorithm, the decision attributes are classified according to the condition attributes. The judgment process is from the root node to the leaf node. Each branch of the tree takes the form of selecting the best split attribute. However, this classification method of decision tree makes it rely too much on training data. If the data are more complicated, there are noisy data, incomplete data, etc. The decision tree will often have overfitting problems. This study mainly analyzes the random forest algorithm model and the CART algorithm and applies the CART algorithm to the model according to the random forest model. Aiming at the algorithm’s shortcomings in solving big data, this study will improve the algorithm through the MapReduce programming model to achieve parallelization of the process and construction of the function. Combining the construction goals and principles of the talent supply chain management system, this study constructs the overall framework and operational process of the enterprise talent supply chain management system based on the decision tree model from the overall level and the operational level. Aiming at the enterprise’s talent management problems, it focuses on designing integrated management, flexible management, talent information integrated management, and evaluation and optimization management models to ensure that the constructed system is operable and measurable and can achieve dynamic optimization. Based on the current situation of talent management in a company, this study analyzes the enterprise talent supply chain management model based on the decision tree model proposed in this study and constructs the overall framework and core model of a company’s talent supply chain management system. The current situation of the company puts forward the safeguard measures for the implementation of the management system to assure that the established management system can be effectively implemented.

Highlights

  • With the development of economic globalization and the promotion of national industrial upgrading, enterprises have ushered in new opportunities and challenges [1]

  • By comparing the time complexity and test accuracy of the traditional Bayesian decision tree algorithm and the classification and regression tree (CART) algorithm based on random forest, it shows that the method proposed in this study has good practical performance and has a useful application effect on incremental data

  • From the data analysis of the experiment, it can be observed that the CART algorithm based on random forest proposed in this study has stronger feasibility in incremental data classification, compared with the Bayesian decision tree algorithm

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Summary

Jiaonan Han

Human Resources Business Consulting Department, Kunlun Digital Technology Co., Ltd., Beijing 100007, China. Decision tree algorithm is a widely used classification and prediction method. Each branch of the tree takes the form of selecting the best split attribute This classification method of decision tree makes it rely too much on training data. Combining the construction goals and principles of the talent supply chain management system, this study constructs the overall framework and operational process of the enterprise talent supply chain management system based on the decision tree model from the overall level and the operational level. Based on the current situation of talent management in a company, this study analyzes the enterprise talent supply chain management model based on the decision tree model proposed in this study and constructs the overall framework and core model of a company’s talent supply chain management system. Based on the current situation of talent management in a company, this study analyzes the enterprise talent supply chain management model based on the decision tree model proposed in this study and constructs the overall framework and core model of a company’s talent supply chain management system. e current situation of the company puts forward the safeguard measures for the implementation of the management system to assure that the established management system can be effectively implemented

Introduction
Journal of Mathematics
Method
Basic information of management personnel and related personal data
Data fragmentation Collection of decision tree classifiers Build a forest
Number of samples
After system optimization
Full Text
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