Stacking of Hyperparameter Tuned Models for Tagging Coding Problems
This work addresses the need for an AI system to assist students and professionals in practicing coding problems, though it appears incremental in its approach.
The authors tackled the problem of tagging coding problems by proposing a stacking model of hyperparameter-tuned boosting models, achieving 77.8% accuracy and 0.815 PR-AUC on a dataset scraped from Codeforces and Leetcode.
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.