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Stacking of Hyperparameter Tuned Models for Tagging Coding Problems

arXiv 2306.10077 ↗First posted 16 Jun 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Coding problems are problems that require a solution in the form of a computer program. Coding problems are popular among students and professionals as it enhances their skills and career opportunities. An AI system that would help those who practice coding problems would be highly useful and there is a huge potential for such a system. In this work, we propose a model which uses stacking of hyperparameter tuned boosting models to achieve impressive metric scores of 77.8% accuracy and 0.815 PR-AUC on the dataset that was scraped from Codeforces and Leetcode. We open source the dataset and the models developed for this work.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 16 Jun 2023.

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References · 6

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 199
  3. 2019Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAScited by 83
  4. 2019Predicting Algorithm Classes for Programming Word Problemscited by 2
  5. 2019Multi-label Classification for Automatic Tag Prediction in the Context of Programming Challengescited by 1
  6. 2023Predicting Tags For Programming Tasks by Combining Textual And Source Code Datacited by 1

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