Overfitting and Underfitting
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Underfitting and Overfitting
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Imagine you had developed a model that predicts some output. The goal of any model is to generate a correct prediction and avoid incorrect predictions. But how can we be sure that predictions are as good as they can possibly be?
Now constrain your imagining to a classification task (other tasks have similar properties but I find classification easiest to reason about). We use some data to train the model. The result of the training process will be a decision boundary. I.e. class A on one side, class B on another.
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