Overfitting can lead us to make decisions based on irrelevant information from "summary" of Algorithms to Live By by Brian Christian,Tom Griffiths
Overfitting occurs when a model becomes so finely tuned to the data on which it is trained that it starts to pick up on noise rather than signal. This can happen when there is noise or randomness in the data, leading the model to learn patterns that are not actually there. As a result, the model may perform very well on the training data but poorly on new, unseen data. When we overfit a model, we are essentially memorizing the training data rather than generalizing from it. This can be a problem in decision-making because it means we are making decisions based on patterns that are not truly indicative of the underlying reality. In other words, we are mistaking noise for signal and acting on irrelevant information. This concept can be applied to many real-world situations. For example, in hiring decisions, if we focus too much on specific traits or experiences that are present in the candidates we have seen so...
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