In the field of machine learning, especially in image, text, and sequence processing, neural networks have had more success than classical models. The popularity of neural networks has led to several attempts to find an alternative. Deep forest structures have emerged as one of them. The authors have integrated the concept of deep forest into a model called gcForest. It is based on robust random forests that are connected into a cascade.
Structure excels at learning from small data sets and does not consume many computer resources. We examine the building blocks of the structure and introduce changes that could improve performance. In the next step, we program a custom library for decision trees, random forests, and the structure with the proposed changes. Then we test the parts of the implementation separately and compare the results with those of the authors on four data sets. Since we obtain worse results, we provide an explanation. Finally, we talk about the next steps of the implementation.
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