Randomness Factor

The "randomness factor" in machine learning encompasses the influence of stochastic elements—like data shuffling, weight initialization, and random number generators—on model performance, reproducibility, and fairness. Current research focuses on mitigating the negative impacts of randomness through techniques such as ensembling, noise regularization, and controlled randomness injection, often applied to models like random forests, deep neural networks, and graph neural networks. Understanding and controlling this randomness is crucial for building reliable, robust, and fair machine learning systems, improving both the trustworthiness of scientific results and the safety and efficacy of real-world applications.