Stack Overflow上机器学习相关问题的大规模实证研究

为了调查机器学习相关主题分布和发展趋势，从在线问答网站Stack Overflow上，利用过滤标签，从4 178多万帖子中提取出60 028个与机器学习相关的问题帖. 通过分析问题帖，统计各个机器学习平台的讨论量，发现Scikit-learn、TensorFlow、Keras是前3位频繁被讨论的机器学习平台，占总讨论量的58%. 为了进一步分析机器学习相关讨论主题，进行潜在狄利克雷分布（LDA）主题模型训练，提出自适应LDA中的主题数渐进搜索方法，采用主题一致性系数评估输出结果，获得主题最佳数量，从而发现9个讨论主题，分属3个类别：代码相关、模型相关、理论相关. 基于主题中问题帖的浏览数、评论数，分析不同主题的流行度和回答困难程度.

Large-scale empirical study on machine learning related questions on Stack Overflow

By using filtered tags, 60 028 machine learning related questions were extracted from more than 41.78 million posts on an online Q & A website, Stack Overflow, in order to investigate the topic distribution and trends related to machine learning. Extracted question posts were analyzed by counting the amount of discussion on each machine learning platform, and top three most frequently discussed machine learning platforms were discovered, i.e. Scikit-learn, TensorFlow and Keras, accounting for 58% of these posts. Latent Dirichlet allocation (LDA) topic model training was conducted to further explore discussion topics related to machine learning. A progressive search approach was proposed for number of topics in adaptive LDA, which discovered the optimal number of topics with topic coherence coefficient, in order to obtain the optimal topic numbers for LDA models. Nine discussion topics related to machine learning were discovered, which fell into three broad categories, i.e. code-related, model-related, and theory-related. In addition, the popularity and difficulty of different topics were analyzed according to the view counts and comment counts of question posts.