{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/xbnet-an-extremely-boosted-neural-network","title":"XBNet : An Extremely Boosted Neural Network","arxiv_id":"2106.05239","date":"2021-06-09","proceeding":null,"authors":["Tushar Sarkar"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2106.05239v3","url_pdf":"https://arxiv.org/pdf/2106.05239v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"xbnet-an-extremely-boosted-neural-network","repo_url":"https://github.com/tusharsarkar3/XBNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"diabetes-prediction","task_name":"Diabetes Prediction"},{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"survival-prediction","task_name":"Survival Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/breast-cancer-detection-on-breast-cancer-1","task":"Breast Cancer Detection","dataset":"Breast cancer Wisconsin_class 4","model":"XBNET","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"96.49","Average Precision":"0.95"},"uses_additional_data":false},{"leaderboard":"/sota/diabetes-prediction-on-diabetes","task":"Diabetes Prediction","dataset":"Diabetes","model":"XBNET","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"78.78"},"uses_additional_data":false},{"leaderboard":"/sota/fraud-detection-on-kaggle-credit-card-fraud","task":"Fraud Detection","dataset":"Kaggle-Credit Card Fraud Dataset","model":"XBNET","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"71.33"},"uses_additional_data":false},{"leaderboard":"/sota/general-classification-on-iris","task":"General Classification","dataset":"iris","model":"XBNET","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"100"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}