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 far, we may end up hiring someone who is not actually the best fit for the job. This is because we are overfitting to the data we have, rather than considering the broader range of possibilities.
In the realm of algorithms, overfitting can lead to poor performance on new data and a failure to adapt to changing circumstances. It is important to strike a balance between fitting the data well and generalizing from it. This is known as the bias-variance tradeoff, where bias refers to how well the model fits the training data and variance refers to how well it generalizes to new data.
In summary, overfitting can be detrimental to decision-making because it can lead us to focus on irrelevant information and make choices that are not truly reflective of the underlying reality. It is important to be aware of this phenomenon and strive for models that strike a balance between fitting the data and generalizing from it.
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