video1.0<iframe src="https://www.loom.com/embed/c81fa95988ab480787dc769643e38876" frameborder="0" width="1152" height="864" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>8641152Loomhttps://www.loom.com8641152https://cdn.loom.com/sessions/thumbnails/c81fa95988ab480787dc769643e38876-4d820fc82f134138.gif638.012In this video, I discuss my contributions to the Arvis Python library, which is designed for analyzing bias in models. I specifically added a function called "bias vector," which helps compare two models and determine which one fits our data better based on a competition score. I explain the computation and visualization aspects of this function, including how we handle different data types and plot the results. I encourage viewers to explore the merged pull request, which contains 273 lines of code, and to consider how they might utilize the bias vector in their own projects. Your feedback and thoughts on this implementation would be greatly appreciated!