{"version":"1.0","type":"rich","title":"Gist 1c87e6026e22589f1729","description":"This visualization tracks the misclassification rate of a stochastic gradient descent classifier over 5,000 iterations, with separate lines for training and test datasets. The code uses Python with NumPy, Pandas, and Seaborn’s `color_palette` for styling, while Matplotlib’s `plot` and `legend` functions render the two curves. The y-axis shows misclassification rate and the x-axis shows iteration counter, revealing how the model’s performance evolves during a single pass through the data with a constant step size.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-1c87e6026e22589f1729?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}