Stacking of Hyperparameter Tuned Models for Tagging Coding Problems

Autor:	TS, Sathya Krishnan, Pandian, S. Lakshmana, Shunmugapriya, P.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Error corrections have to be made for certain metrics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.10077 Zobrazit plný text záznamu View this record from Arxiv