Maximize ML Model Performance with Data-IQ Framework

Discover how to enhance machine learning model performance using the Data-IQ framework in this 12-minute video presentation by Nabeel Seedat, a PhD student at the University of Cambridge. Learn about a novel approach to systematically stratify data examples into subgroups based on their outcomes, allowing for comprehensive auditing of tabular, image, or text data with minimal code implementation. Explore how analyzing individual example behavior during training, focusing on predictive confidence and aleatoric uncertainty, enables the categorization of data into Easy, Ambiguous, and Hard subgroups. Understand the framework's robustness across different models and its applications in feature acquisition, dataset selection, and reliable model usage. Gain insights into the significant impact of the Ambiguous subgroup on model generalization and discover how Data-IQ can be applied to various ML models, including neural networks and gradient boosting techniques.