Group Wise Prediction Model

Group-wise prediction models aim to improve the accuracy and fairness of machine learning by leveraging the similarities between individuals or data points. Current research focuses on developing methods to effectively group data (e.g., using model-based clustering or multilevel optimization) and then training separate, yet potentially interconnected, models for each group, leading to better predictive performance than individual or single-model approaches. This approach addresses challenges like data sparsity and bias, offering benefits in diverse fields such as personalized medicine, natural language processing, and fair AI systems. The resulting models can provide more accurate predictions and mitigate unfair outcomes by accounting for group-specific characteristics.