Machine Learning  Decision Boundary Algorithms Decision Boundary Algorithms Concepts Decision boundary algorithms are used in machine learning to create a boundary that separates different classes or groups in a dataset. They are used to classify data points based on their features or attributes. Some popular decision boundary algorithms include decision trees, random forests, logistic regression, and support vector machines.   One example of a decision boundary algorithm is the logistic regression algorithm. To determine the likelihood of a binary outcome (such as "yes" or "no"), a binary classification procedure known as logistic regression is utilized(yes/no, true/false). It creates a decision boundary by fitting a logistic function to the training data.   Let's consider the example of a dataset containing information about a bank's customers, including their age and credit score, as well as whether they have defaulted on a loan. We can use logistic r