Deep Forest

Deep Forest is a machine learning approach that builds upon the established random forest algorithm by creating deep, cascading layers of trees to improve predictive accuracy, particularly for complex datasets. Current research focuses on optimizing Deep Forest architectures through techniques like layerwise data augmentation, efficient hardware acceleration (e.g., FPGA implementation), and refined feature selection methods to reduce computational cost and improve interpretability. These advancements aim to enhance the performance and applicability of Deep Forest across diverse fields, from high-energy physics simulations to tabular data classification and even quantum computing applications, where it shows promise in tasks like surface roughness prediction.