FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches. (arXiv:2103.14539v3 [cs.LG] UPDATED)

The machine learning (ML) life cycle involves a series of iterative steps,
from the effective gathering and preparation of the data, including complex
feature engineering processes, to the presentation and improvement of results,
with various algorithms to choose from in every step. Feature engineering in
particular can be very beneficial for ML, leading to numerous improvements such
as boosting the predictive results, decreasing computational times, reducing
excessive noise, and increasing the transparency behind the decisions taken
during the training. Despite …

analytics arxiv engineering feature engineering