In #machinelearning, a decision boundary is the statistical delineation between possible decisions. The decision boundary is like a threshold for translating statistical outcomes into real-world decisions. You start by using known data to create the decision boundary. Once you have that, you can compare unknown data to your decision boundary to solve real-world problems.
For example, in a system to recognize spam messages, the system would use known data to create a decision boundary between "spam" and "not spam". Then, when a new message comes in, it would be analyzed and compared to the decision boundary to determine if the new message is spam or not.
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