featuresillustrate

Featuresillustrate is a visualization toolkit and framework designed to help data scientists and researchers analyze and communicate the features that underpin data-driven models. It emphasizes transparent feature engineering by providing interactive tools to inspect, compare, and explain features across datasets and models.

Core capabilities include interactive charts for numeric and categorical features, feature attribution visualizations using methods such as SHAP and permutation importance, and dimensionality-reduction views to explore high-dimensional feature spaces. The toolkit supports distributions, relationships, correlations, and the progression of features through training pipelines, enabling side-by-side comparisons of feature sets and model explanations.

It integrates with common machine learning stacks and data formats, offering adapters for Python libraries like scikit-learn, PyTorch, and TensorFlow, as well as R. Outputs can be exported as images or notebook cells, and dashboards can be embedded in reports or shared online to support collaboration and reproducibility.

Typical use cases include data exploration and feature selection, model auditing and bias detection, education and training, and documentation of feature engineering decisions. The project emphasizes accessibility, reproducibility, and lightweight dependencies to fit into existing workflows.

Featuresillustrate originated as an open-source project in the data-visualization community and is released under an open license. It is maintained by a growing community of contributors with comprehensive documentation and example notebooks.

See also: feature engineering, data visualization, model interpretability, SHAP.