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.
A model that is underfit will produce evaluation metrics that are poor on the training data alone, such as high RMSE or misclassification rate.
Bias-variance tradeoff is a fundamental concept in supervised machine learning that describes the relationship between a model’s ability to fit the training data and its ability to generalize to new, unseen data.
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