Widrow-Hoff algorithm

The Widrow-Hoff algorithm, also known as the Least Mean Squares (LMS) algorithm, is a method used in machine learning to improve predictions or decisions based on data. It adjusts its parameters gradually to minimize the difference between predicted and actual outcomes. Think of it like tuning a musical instrument: after each note, you slightly tweak the strings to make the sound closer to the desired pitch. Over time, these small adjustments help the system make more accurate predictions, enabling it to learn from its mistakes and improve performance.