Analysis of Mathematical Model and Optimization Strategy based on Deep Forest Algorithm

The purpose of this paper is to discuss the mathematical model construction based on Deep Forest algorithm and its optimization strategy. Through detailed analysis of the basic principle, structure and optimization method of the algorithm, a series of improvement measures are proposed to enhance the performance and stability of the algorithm by combining with cases in practical applications. The research results of this paper have important reference value for promoting the application of Deep Forest algorithm in various fields. Through a series of experiments, the original algorithm, deep reinforcement learning optimization, parameter tuning, and comprehensive optimization strategies were evaluated, and the following conclusions were drawn: After combining deep reinforcement learning and parameter tuning strategies, the average accuracy of the algorithm is improved to 0.91, the average recall reaches 0.89, the average F1 value is 0.90, and the computation time is shortened to 1.75 seconds. From the data results, the comprehensive optimization strategy significantly improves the computational efficiency while maintaining high classification performance. The research in this paper provides an important reference value for the application of Deep Forest algorithm in various fields.