This article discusses the extensive evaluation of quantized large language models (LLMs) by Neural Magic, finding that quantized LLMs maintain competitive accuracy and efficiency with their full-precision counterparts.
This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
Learn about the importance of evaluating classification models and how to use the confusion matrix and ROC curves to assess model performance. This post covers the basics of both methods, their components, calculations, and how to visualize the results using Python.
