Dataset Evolver: An Interactive Feature Engineering Notebook
Dataset Evolver: An Interactive Feature Engineering Notebook
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Dataset Evolver：交互式特征工程笔记本
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Turaga
中科院分区：
文献类型：
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作者：
F. Nargesian;Udayan Khurana;Tejaswini Pedapati;Horst Samulowitz;D. Turaga
我们提出了DATASET EVOLVER，一个交互式的基于笔记本的工具，以支持数据科学家执行分类任务的特征工程。它基于自动特征工程算法，为用户提供关于要构建的新特征的建议。用户可以以不同的方式浏览给定的选择，验证影响，并有选择地接受建议。DATASET EVOLVER是一个可插入的功能工程框架，可以添加几个探索策略。它目前包括基于元学习的探索和基于强化学习的探索。建议的功能是使用定义良好的数学函数构造的，并且很容易解释。我们的系统提供了一个混合的主动系统的用户正在协助一个自动化代理，以有效地解决复杂的问题的特征工程。它将数据科学家的工作从几小时减少到几分钟。
We present DATASET EVOLVER, an interactive Jupyter notebook-based tool to support data scientists perform feature engineering for classification tasks. It provides users with suggestions on new features to construct, based on automated feature engineering algorithms. Users can navigate the given choices in different ways, validate the impact, and selectively accept the suggestions. DATASET EVOLVER is a pluggable feature engineering framework where several exploration strategies could be added. It currently includes meta-learning based exploration and reinforcement learning based exploration. The suggested features are constructed using well-defined mathematical functions and are easily interpretable. Our system provides a mixed-initiative system of a user being assisted by an automated agent to efficiently and effectively solve the complex problem of feature engineering. It reduces the effort of a data scientist from hours to minutes.