Widrow-Hoff Rule

The Widrow-Hoff Rule, also known as the Least Mean Squares (LMS) algorithm, is a method used in machine learning for adjusting the weights of a model to minimize errors. Imagine you're trying to predict the outcome of a situation, like guessing the score of a basketball game, and your guesses are off. The Widrow-Hoff Rule helps you gradually update your predictions based on past errors, so you can improve over time. It’s like fine-tuning a radio to get a clearer signal, helping the model learn and adapt to provide better results.