ML Algorithm

Machine learning (ML) algorithms are computational tools designed to learn patterns from data and make predictions or decisions without explicit programming. Current research emphasizes improving algorithm performance through techniques like integrating behavioral data to enhance prediction accuracy (e.g., in online learning) and developing frameworks for efficient distributed training (e.g., in federated learning across relational databases). Key algorithms under investigation include XGBoost, Random Forest, Support Vector Machines, and neural networks, with a focus on optimizing hyperparameters and comparing algorithm performance across diverse datasets and metrics. These advancements have significant implications for various fields, improving prediction accuracy in diverse applications and enabling efficient data analysis in distributed settings.