data imbalance
data imbalance on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on data imbalance in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around data imbalance, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
About the impact of grouping classes in multiclass classification [D]
Addressing data scarcity in multiclass classification is a common challenge. Grouping infrequent classes into a "catch-all" category, like "Other breed" in dog breed classification, can introduce complexities. While seemingly pragmatic, this approach may force models to learn convoluted decision boundaries, potentially hindering overall performance. An alternative—and often more effective—strategy involves treating these instances as out-of-distribution samples, focusing training data on well-represented classes. Consider "Trained an diffusion model that runs on 264KB of RAM," demonstrating innovative approaches to resource constraints in AI.
3 Collapsing models [R]
Training multiple models for BIRADS detection presents a common challenge: collapse towards the dominant class, in this case, BIRADS 1. User /u/Rihitwo is experiencing this with three models trained on the VinDR dataset, utilizing cross-entropy and center loss with class weights. The likely culprit is the dataset’s significant imbalance. Consider exploring alternative loss functions or advanced data augmentation techniques to mitigate this bias. For a deeper dive into handling complex model outputs, see our article, "How to Implement Structured Output with Local LLMs."