{"version":"1.0","type":"rich","title":"Fork of HTML Starter","description":"This interactive explorer shows how different visual encodings affect the interpretation of synthetic machine-learning configuration data, including training time, accuracy, and model size. Five toggleable versions demonstrate position, categorical hue, quantitative area, a deliberately broken encoding, and a repaired chart with an outlier highlight. The visualization uses D3 v7’s `d3.csv` to load data from a GitHub gist and renders SVG circles with color scales and size mappings. Highlighted features include ranking by model family and marking the highest-accuracy configuration, with data and code available under the MIT license.","provider_name":"VizHub AI Studio","provider_url":"https://vizhub.com","html":"<iframe src=\"https://vizhub.com/viz/8dd9c5563263476dbb83eb051c56cdef?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"0\"></iframe>","width":960,"height":500,"cache_age":3600}