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Consider a binary classification problem. Suppose i have trained a model on a linearly separate training set, and now i get a new labeled data point which is correctly classified by the model and far away from the decision boundary. If i now add this new point to my earlier training set and re-train, in which cases is the learnt decision boundary likely to change?

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