Model Accuracy

Model accuracy, a crucial metric in machine learning, assesses a model's ability to correctly predict outcomes. Current research focuses on improving accuracy by addressing data limitations, such as noisy labels and data heterogeneity in federated learning, and by developing more efficient training and evaluation methods, including techniques like coreset selection and stratified sampling. These advancements are vital for enhancing the reliability and trustworthiness of machine learning models across diverse applications, from object recognition to financial forecasting, and for optimizing model deployment in resource-constrained environments. Furthermore, research is actively exploring methods to quantify and mitigate biases that can lead to disparate impacts on model accuracy across different subgroups.