FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

arXiv:2103.14539v4 Announce Type: replace
Abstract: 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 …

abstract algorithms analytics arxiv cs.hc cs.lg data engineering every extraction feature feature engineering improvement iterative life life cycle machine machine learning presentation processes results series stat.ml type visual visual analytics