XBNet : An Extremely Boosted Neural Network

9 Jun 2021  ·  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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Breast Cancer Detection Breast cancer Wisconsin_class 4 XBNET Accuracy 96.49 # 1
Average Precision 0.95 # 1
Diabetes Prediction Diabetes XBNET Accuracy 78.78 # 1
General Classification iris XBNET Accuracy 100 # 1
Fraud Detection Kaggle-Credit Card Fraud Dataset XBNET Accuracy 71.33 # 1
Survival Prediction Titanic XBNET Accuracy 79.85 # 1


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