What does a 'decision boundary' separate in a dataset?

A decision boundary is a critical concept used in classification algorithms in machine learning. It essentially defines how the model differentiates between different classes within the dataset. Specifically, it separates positive and negative classes, which can be conceptualized as distinct regions in the feature space.

When a classification algorithm learns from the training data, it identifies patterns that help it distinguish between classes. The decision boundary is then established in relation to these patterns, enabling the algorithm to classify new, unseen instances into the correct category based on their features. For instance, in a binary classification task, the decision boundary will demarcate the area where inputs are classified as belonging to the positive class from those classified as the negative class.

Understanding this concept is crucial, as it directly influences the model's performance. A well-defined decision boundary leads to accurate classifications, while a poorly defined one may result in misclassifications, impacting overall predictive success. The notion of decision boundaries underpins various classification techniques, where the boundary might manifest as a straight line (as in linear classifiers) or as a more complex structure (as in decision trees or neural networks).