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.

本文提出使用超参数调节提高准确率的增强模型堆叠来帮助编码问题的练习，取得了 77.8％的准确率和 0.815 的 PR-AUC，并公开了数据集和模型。

使用超参数调整的模型叠加技术进行编码问题标注

Stacking of Hyperparameter Tuned Models for Tagging Coding Problems

We propose two frameworks to deal with problem settings in which both
structured and unstructured data are available. Structured data problems are
best solved by traditional machine learning models such as boosting and
tree-based algorithms, whereas deep learning has been widely applied to
problems dealing with images, text, audio, and other unstructured data sources.
However, for the setting in which both structured and unstructured data are
accessible, it is not obvious what the best modeling approach is to enhance
performance on both data sources simultaneously. Our proposed frameworks allow
joint learning on both kinds of data by integrating the paradigms of boosting
models and deep neural networks. The first framework, the
boosted-feature-vector deep learning network, learns features from the
structured data using gradient boosting and combines them with embeddings from
unstructured data via a two-branch deep neural network. Secondly, the
two-weak-learner boosting framework extends the boosting paradigm to the
setting with two input data sources. We present and compare first- and
second-order methods of this framework. Our experimental results on both public
and real-world datasets show performance gains achieved by the frameworks over
selected baselines by magnitudes of 0.1% - 4.7%.

提出了两个结构化和非结构化数据都存在的问题的框架，采用梯度提升和深度神经网络相结合，可以提高性能。这些框架在各种数据集上进行了实验，表现优于基线方法 0.1％-4.7％。