A decision boundary is a line or surface that separates different classes in a dataset. Imagine you have a scatter plot with two types of points: red and blue. The decision boundary helps us determine which side of the line a new point belongs to, based on its features. For example, if a new point falls on the red side, it is classified as red, while if it’s on the blue side, it’s classified as blue. In machine learning, algorithms like Support Vector Machines or Neural Networks create these boundaries to make predictions. The goal is to find the best decision boundary that minimizes misclassifications, ensuring accurate results when classifying new data points.