Unverified50% confidenceBenchmarkExact time
Grinsztajn等人2022年在NeurIPS发表的基准研究证明,在中等规模表格数据集上,GBDT类模型持续优于深度学习方法
1
Sources
50%
Confidence
Long-term
Relevance
8/5/2026
First Seen
Sources
Related Claims
UnverifiedThe poor scalability of statistical modeling methods on high-dimensional data is the core reason for the gradual rise of deep learning methods.68% similarUnverified许多深度学习模型在权重量化到4位后仍能保持95%以上的推理精度68% similarUnverified流形假设由Bengio、LeCun等深度学习先驱提出,认为高维真实数据近似分布在低维光滑流形上67% similarUnverifiedThe paper published in 2021, 'Why do tree-based models still outperform deep learning on tabular data?', validated that manual feature engineering combined with gradient boosting trees still outperforms end-to-end deep learning solutions in most cases on structured data.66% similarUnverifiedGradient boosting trees (such as XGBoost and LightGBM) have long outperformed deep learning methods on structured data tasks due to the strong feature heterogeneity, limited sample sizes, and lack of local continuity assumptions in tabular data.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/689002API
curl https://kongchang.com/api/v1/knowledge/claims/689002MCP
get_claim(id=689002)