一句话：提出从高质量 Kaggle 流水线中学习映射规则，为未见 ML 流水线自动预测可视化。
可视化对机器学习（ML）流水线很重要，既有助于探索数据，也能解释流水线以提升可理解性。本文提出一种自动生成 ML 流水线可视化的新方法：从高赞 Kaggle 流水线中学习可视化。该方法从高质量人工编写的流水线及其训练数据集中提取代码特征与数据集特征，使用关联规则挖掘（ARM）学习从这些特征到可视化的映射规则，再利用学到的规则为未见的 ML 流水线预测可视化。评估结果表明该方案可行且有效，能够为 ML 流水线生成可视化。
Visualization is very important for machine learning (ML) pipelines because it can show explorations of the data to inspire data scientists and show explanations of the pipeline to improve understandability. In this paper, we present a novel approach that automatically generates visualizations for ML pipelines by learning visualizations from highly-upvoted Kaggle pipelines. The solution extracts both code and dataset features from these high-quality human-written pipelines and corresponding training datasets, learns the mapping rules from code and dataset features to visualizations using association rule mining (ARM), and finally uses the learned rules to predict visualizations for unseen ML pipelines. The evaluation results show that the proposed solution is feasible and effective to generate visualizations for ML pipelines.