Decision Boundaries
Decision boundaries are the limits or borders that separate different classes or categories in a machine learning model. These boundaries are determined by the algorithm based on the input data and the chosen model. The decision boundary can be linear or non-linear, depending on the complexity of the problem and the model used. The goal of the algorithm is to find the decision boundary that best separates the classes and minimizes the classification error. Decision boundaries are important in many applications, such as image recognition, speech recognition, and natural language processing.
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