What is decision boundary?

A decision boundary is a boundary that separates different classes or categories in a dataset in machine learning and statistical analysis. This boundary helps classify new data points into one of the predefined classes based on their features or attributes.

In binary classification problems, a decision boundary separates the data points belonging to different classes. The goal of a classifier is to learn a decision boundary that can accurately classify new data points into the correct class.

Decision boundaries can take different forms, depending on the algorithm used for classification and the complexity of the data. They can be linear, non-linear, or even multidimensional. For example, in linear classification algorithms such as logistic regression or linear support vector machines, the decision boundary is a straight line or hyperplane that separates the classes. In non-linear classification algorithms such as decision trees or neural networks, the decision boundary can be complex and curved.

It's important to note that the performance of a classifier is highly dependent on the quality of the decision boundary learned from the training data. A good decision boundary should generalize well to new data points and minimize errors in classification. Overfitting can occur when a classifier learns a decision boundary that is too complex and adapts to noise in the training data, leading to poor performance on unseen data. Underfitting, on the other hand, occurs when the decision boundary is too simple and fails to capture the underlying patterns in the data.