AR Surface Display for Regression Model Selection
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Solution Overview
Problem
Current visualization techniques for regression analysis lack effective methods for displaying goodness-of-fit and model selection processes, particularly in map-based visualizations, which are essential for understanding model quality and variable importance.
Innovation Solution
The method involves using a combination of a surface display and an augmented reality (AR) wearable display to visualize datasets, allowing users to select variables, fit models, and display goodness-of-fit metrics through glyph layers, enabling detailed inspection of likelihood ratio tests and regional model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional map-based visualization techniques are used for regression analysis, then predictions can be displayed on a map, but goodness-of-fit and model selection processes cannot be effectively visualized
Solution Approach 1:
The visualization system is segmented into multiple display components: a surface display for base map visualization and an AR wearable display for overlaying goodness-of-fit metrics and model selection information. This segmentation allows each display to specialize in specific information types, resolving the contradiction by adding necessary visualizations without overwhelming complexity.
Solution Approach 2:
The patent transitions from traditional 2D map-based visualization to a multi-dimensional visualization system by introducing AR wearable displays that can overlay additional information layers in 3D space. This dimensional expansion enables the simultaneous display of predictions, goodness-of-fit metrics, and model selection parameters without compromising the base map visualization.
2Measurement precision
If detailed model selection processes are visualized, then model quality understanding improves, but visualization complexity increases
Solution Approach 1:
The AR wearable display enables local quality enhancement by allowing users to focus on specific regions of interest on the map and receive detailed goodness-of-fit information only for those areas. This localized approach provides precise model quality measurements without requiring the entire visualization interface to be complex.
Solution Approach 2:
The AR wearable display acts as an intermediary between the base map visualization and the detailed model selection information. It mediates the complexity by filtering and presenting only the most relevant goodness-of-fit metrics and model comparison data to the user, reducing the apparent complexity while maintaining measurement precision.
3Adaptability or versatility
If multiple variables and models are displayed simultaneously, then comprehensive analysis is enabled, but user interface complexity increases
Solution Approach 1:
The visualization system dynamically adapts its content based on user interactions and selections. When users select specific variables or models, the AR wearable display updates to show relevant goodness-of-fit information for those selections. This dynamic behavior enables comprehensive analysis capability while keeping the user interface simple by only displaying what is currently relevant.
Data Source
AI summary
The disclosure relates to a method, surface display, augmented reality (AR) wearable display and system for displaying and fitting a dataset into a model. The method comprises displaying the dataset through a combination of a surface display and an AR wearable display, the dataset comprising a plurality of variables. The method comprises receiving a selection of variables, from the plurality of variables. The method comprises, using the selection of variables, fitting the dataset into a first model. The method comprises displaying a first goodness-of-fit corresponding to the first model and a second goodness-of-fit corresponding to a second model, through the combination of the surface display and the AR wearable display.