### How does a learning curve give insight into whether the model is under- or over-fitting?

A model that is underfit will produce evaluation metrics that are poor on the training data alone, such as high RMSE or misclassification rate.

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The bias/variance tradeoff refers to the challenge of finding a model that both performs at a high level of accuracy on the data on which it is trained (bias) while at the same time generalizes well to unseen data (variance)

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