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

arXiv: https://arxiv.org/abs/2103.14539 · HTML: https://mlsanity.com/paper/2103.14539/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 125 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Feature Engineering for Predictive Modeling using Reinforcement Learning](https://mlsanity.com/paper/1709.07150/) (2017)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [An Empirical Analysis of Feature Engineering for Predictive Modeling](https://mlsanity.com/paper/1701.07852/) (2017)
- [E.T.-RNN: Applying Deep Learning to Credit Loan Applications](https://mlsanity.com/paper/1911.02496/) (2019)
- [Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics](https://mlsanity.com/paper/1907.07296/) (2019)
- [StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics](https://mlsanity.com/paper/2005.01575/) (2020)
- [VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization](https://mlsanity.com/paper/2012.01205/) (2020)
- [FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images](https://mlsanity.com/paper/1908.00671/) (2019)
- [Progressive Data Science: Potential and Challenges](https://mlsanity.com/paper/1812.08032/) (2018)
- [FDive: Learning Relevance Models using Pattern-based Similarity Measures](https://mlsanity.com/paper/1907.12489/) (2019)

## Built on by (most cited first, 7 of 7)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques](https://mlsanity.com/paper/2203.15753/) (2022)
- [Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research](https://mlsanity.com/paper/2308.06290/) (2023)
- [Visual Analytics for Explainable and Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2507.10240/) (2025)
- [DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps](https://mlsanity.com/paper/2304.00133/) (2023)
- [AI Total: Analyzing Security ML Models with Imperfect Data in Production](https://mlsanity.com/paper/2110.07028/) (2021)
- [MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels](https://mlsanity.com/paper/2212.03539/) (2022)

## Abstract (arXiv)

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 and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.