Stratified Sampling

Stratified sampling is a statistical technique that divides a population into subgroups (strata) before sampling, aiming to improve the accuracy and efficiency of estimations compared to simple random sampling. Current research focuses on applying stratified sampling to enhance various machine learning methods, including improving the reliability of model evaluations, mitigating artifacts in explainable AI techniques, and optimizing gradient descent algorithms for faster convergence. These advancements are significant because they lead to more robust and reliable results in diverse fields, from natural language processing and computer vision to reinforcement learning and data valuation, ultimately improving the trustworthiness and efficiency of data analysis and machine learning models.