Full interpretable machine learning in 2D with inline coordinates
This work addresses interpretability in machine learning for end-users, though it appears incremental as it builds on existing 2D visualization techniques.
The paper tackles the problem of interpreting high-dimensional machine learning by proposing a full 2D methodology using inline coordinates to discover n-D patterns without information loss, demonstrated through a successful benchmark case study.
This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. Specifically, it can be done with the inline based coordinates in different modifications, including static and dynamic ones. The classification and regression algorithms based on these inline coordinates were introduced. A successful case study based on a benchmark data demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning as a promising ML methodology. It has advantages of abilities to involve actively the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.