The application of RF algorithm integrated with F-measure in the quality evaluation of agricultural personnel training

Abstract

The basis for societal progress and development is the caliber of people training provided by colleges and universities. In the context of the development of agricultural modernization, a key assurance for fostering the growth of agricultural modernization is the caliber of the staff training provided in agricultural colleges and universities. At present, the existing evaluation models for the quality of personnel education in agricultural universities have defects such as low efficiency in data processing and long running time. Therefore, to better judge the training quality of agricultural graduates in colleges and universities, the study proposes to use the random forest algorithm to build an evaluation method for the quality of agricultural talent training, and on this basis, use the TRRF algorithm to improve it. At the same time, to weight the data, the F-measure assessment method is applied. In this way, the effectiveness of the evaluation model can be improved. The experimental findings demonstrate that the F-TRRF model suggested in the study has 99.17% accuracy when evaluating the standard of agricultural staff training. Therefore, the random forest evaluation model integrated with F-measure suggested in the study has a high level of precision in evaluating the training quality of agricultural talents, which can effectively meet the actual needs of talent evaluation, provide a basis for cultivating more and higher quality agricultural talents, and provide reference opinions on talent cultivation.