Finally, another problem is class imbalance derived from basic and advanced transformations. To manage the resulting class imbalance, two further distinct balanced datasets are generated: i) one ...

Abstract: Data augmentation is crucial for addressing insufficient training data, especially for augmenting positive samples. However, existing methods mostly rely on neural network-based feedback for ...

The Common Data Set can help prospective students know how much aid they could get to pay for college. Why don’t all schools provide it? By Ron Lieber A similar version of this column was published ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

If you’d like an LLM to act more like a partner than a tool, Databot is an experimental alternative to querychat that also works in both R and Python. Databot is designed to analyze data you’ve ...

Why write SQL queries when you can get an LLM to write the code for you? Query NFL data using querychat, a new chatbot component that works with the Shiny web framework and is compatible with R and ...

A Python tool for managing YOLO datasets, including YOLOv5, YOLOv8, YOLOv11 and other Ultralytics-supported models. It streamlines tasks like dataset combination, data augmentation, class removal, and ...

A Python tool for managing YOLO datasets, including YOLOv5, YOLOv8, YOLOv11 and other Ultralytics-supported models. It streamlines tasks like dataset combination, data augmentation, class removal, and ...

Abstract: Label imbalance and data scarcity in Natural Language Processing (NLP) pose significant challenges to the development of effective text classification models. One approach to solve label ...