Dataset Evolver: an Interactive Feature Engineering Notebook
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.
Code
未发现
Tasks
classification tasks
Datasets
未发现
Problems
feature engineering for classification tasks
Methods
interactive Jupyter notebook, automated feature engineering algorithms, meta-learning based exploration, reinforcement learning based exploration
Results from the Paper
reduces data scientist effort from hours to minutes, easily interpretable features