Decision Boundary
In machine learning, a decision boundary is a hypersurface that separates the input space into different classes. It is a mathematical function that maps the input features to class labels. The decision boundary is learned during the training process of a machine learning model and is used to make predictions on new, unseen data. The goal of the learning algorithm is to find the decision boundary that best separates the classes, based on the training data. The decision boundary can be linear or non-linear, depending on the complexity of the problem and the type of algorithm used.
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