Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making.
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.
As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works.
The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application.
In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable.
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions.