The role of LightGBM model in management efficiency enhancement of listed agricultural companies

Abstract

Abstract This paper first explores the principle of the the LightGBM model, which uses a gradient-based one-sided sampling GOSS procedure to sample the sample data to ensure the accuracy of the procedure in information gain evaluation. The mutually exclusive feature bundling Histogram procedure is used to bind the feature variables of the data and the Leaf-wise strategy for differential splitting to enhance the training speed of the model. Then the steps of the LightGBM procedure are discussed, using a a decision tree as the base learner, then iterative training in sequence, and greedy learning using forward distributed procedure and the original feature fetches must be identified from the bundled features to reduce the training complexity. Finally, the LightGBM procedure is associated and investigated with the other four procedures in terms of discriminative accuracy and discriminative time of the factors affecting the efficiency of enterprise management. In terms of level discrimination accuracy, the level discrimination rate of the LightGBM model is basically above 0.7, and the discrimination rate reaches 0.9 at level 6, which is better than other procedures. The discriminatory time of the LightGBM model is only 28 seconds, which is about 16 times faster than the CART model and 20 times faster than the support vector machine model.