  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 that, while several visual analytics tools exist
to monitor and control the different stages of the ML life cycle (especially
those related to data and algorithms), feature engineering support remains
inadequate. In this paper, we present FeatureEnVi, a visual analytics system
specifically designed to assist with the feature engineering process. Our
proposed system helps users to choose the most important feature, to transform
the original features into powerful alternatives, and to experiment with
different feature generation combinations. Additionally, data space slicing
allows users to explore the impact of features on both local and global scales.
FeatureEnVi utilizes multiple automatic feature selection techniques;
furthermore, it visually guides users with statistical evidence about the
influence of each feature (or subsets of features). The final outcome is the
extraction of heavily engineered features, evaluated by multiple validation
metrics. The usefulness and applicability of FeatureEnVi are demonstrated with
two use cases, using a popular red wine quality data set and publicly available
data related to vehicle recognition from their silhouettes. We also report
feedback from interviews with ML experts and a visualization researcher who
assessed the effectiveness of our system.
