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

Abstract

Fig. 1. Selecting important features, transforming them, and generating new features with FeatureEnVi: (a) the horizontal beeswarm plot for manually slicing the data space (which is sorted by predicted probabilities) and continuously checking the migration of data instances throughout the process; (b) the table heatmap view for the selection of features according to feature importances calculated from automatic techniques; (c) the radial tree providing an overview of the features with statistical measures for the different groups of instances, as set by the user-defined data slices; (d) the graph visualization for the detailed exploration of features, their transformation, and comparison between two or three features for feature generation purposes; and (e) the punchcard for tracking the steps of the process and the grouped bar chart for comparing the current vs. the best predictive performance based on three validation metrics.

AI Total: Analyzing Security ML Models with Imperfect Data in Production

“…A good amount of work features visualizations that focus on model training and parameter tuning [10,22]. There are important works on direct error examination [2], data validation [9,17] and label validation [11] but they all focus on the development phase alone.…”