{"version":"1.0","type":"rich","title":"Gist c432d79c438c772e0332","description":"This visualization demonstrates a regression analysis workflow from a Julia notebook, showing how a random feature map (phi) transforms 2D samples into a 250-dimensional space for least-squares fitting. The code computes the optimal weight vector w using normal equations with ridge regularization, evaluating the objective function across different lambda values. It uses Julia’s linear algebra operations (`transpose`, `\\` for matrix division, `eye`) and `randn` for Gaussian sampling, with SVG rendering and animation to display the fitted model’s error surface and weight trajectories.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-c432d79c438c772e0332?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}