neurodata/df-dn-paper
Conceptual & empirical comparisons between decision forests & deep networks
This project helps machine learning researchers and practitioners understand when and why deep neural networks outperform decision forests, especially with limited data. It provides a framework for comparing these two powerful modeling techniques, taking in various datasets and producing insights and benchmark figures on their relative performance and characteristics. This is ideal for those evaluating machine learning models for scientific studies, predictive analytics, or classification tasks.
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Use this if you are a machine learning practitioner or researcher trying to decide between using deep neural networks or decision forests for your predictive modeling tasks, especially when dealing with smaller datasets.
Not ideal if you are looking for a plug-and-play solution for building models, as this project focuses on research and comparison rather than direct application.
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18
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8
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Jupyter Notebook
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Last pushed
May 30, 2025
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