HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models
This work addresses the problem of interpretable model evaluation for ML practitioners, though it appears incremental as it builds on existing statistical and visual methods.
The paper tackles the challenge of evaluating machine learning models through hypothesis testing by introducing HypoML, a visual analytics tool that combines statistical hypothesis testing with logical reasoning to test multiple hypotheses about how extra information affects models, and demonstrates its intuitive and explainable nature in applications.
In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a "concept" or "feature" may benefit or hinder a ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing data is transformed to a visual representation for rapid observation of the conclusions and the logical flow between the testing data and hypotheses.We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.