Papers › XBNet : An Extremely Boosted Neural Network
XBNet : An Extremely Boosted Neural Network
Tushar Sarkar
Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio. However, it has been observed that their performance is not up to the mark in tabular data; hence tree-based models are preferred in such scenarios. A popular model for tabular data is boosted trees, a highly efficacious and extensively used machine learning method, and it also provides good interpretability compared to neural networks. In this paper, we describe a novel architecture XBNet, which tries to combine tree-based models with that of neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data which increases its interpretability and performance.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Breast Cancer Detection | Breast cancer Wisconsin_class 4 | XBNET | Accuracy | 96.49 | #1 of 1 | Archive leaderboard | report |
| Breast Cancer Detection | Breast cancer Wisconsin_class 4 | XBNET | Average Precision | 0.95 | #1 of 1 | Archive leaderboard | report |
| Diabetes Prediction | Diabetes | XBNET | Accuracy | 78.78 | #1 of 1 | Archive leaderboard | report |
| Fraud Detection | Kaggle-Credit Card Fraud Dataset | XBNET | Accuracy | 71.33 | #2 of 2 | Archive leaderboard | report |
| General Classification | iris | XBNET | Accuracy | 100 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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