Info about the Project
Visual analytics (VA) systems with semantic interaction help users craft machine learning (ML) based solutions in various domains such as bio-informatics, finance, sports, etc. However current semantic interaction based approaches are data and task-specific which might not generalize across different problem scenarios. In this project, we describe a novel technique of abstracting user intents and goals in the form of an interactive objective function which can guide any auto-ML based model optimizer (such as Hyperopt, Sigopt, etc.) to construct classification models catering to the expectation
Faculty: Alex Endert
Students: Subhajit Das
Visual analytics (VA) systems with semantic interaction help users craft machine learning (ML) based solutions in various domains such as bio-informatics, finance, sports, etc. However current semantic interaction based approaches are data and task-specific which might not generalize across different problem scenarios. In this project, we describe a novel technique of abstracting user intents and goals in the form of an interactive objective function which can guide any auto-ML based model optimizer (such as Hyperopt, Sigopt, etc.) to construct classification models catering to the expectation
Faculty:
Alex Endert
Students:
Subhajit Das
