Decision Boundary Visualization

Decision boundary visualization is a technique used in machine learning to illustrate the boundaries that separate different classes in a classification problem. It helps in understanding how a model makes predictions based on input features, providing insights into the decision-making process of algorithms. By plotting the decision boundaries on a graph, practitioners can visualize how well the model performs, identify areas of overlap between classes, and detect potential issues such as overfitting or underfitting. This visualization is particularly useful for evaluating the performance of models in two-dimensional feature spaces, making it easier to communicate results to stakeholders and improve model design.