Spinning

Spinning is a training process used in machine learning to improve a model’s performance by reducing the impact of biases and variance. It involves training multiple models or parts of a model on different subsets of data or with varied settings, then combining their results to produce a more accurate, stable prediction. Think of it like consulting several experts and then averaging their advice to make a better decision. Spinning helps ensure the model generalizes well to new, unseen data, making it more reliable and robust.