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Overfitting occurs when a model performs well on training data but poorly on new data from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Overfitting is a common problem faced when training predictive models. It happens when a model becomes too complex and starts to learn the noise in the training data rather than the actual underlying patterns. This can lead to a situation where the model performs exceedingly well on the training data but fails miserably when presented with new, unseen data. In other words, the model has essentially memorized the training data rather than truly learning from it. One way to understand overfitting is to think of it as fitting the training data too closely, to the point where the model starts to capture the random fluctuations and outliers in the data. These fluctuations and outliers are unique to the training data and are not representative of the general patterns that the model should be learning. As a result, when the model is tested on new data, it struggles to generalize beyond the specific quirks of the training set. Overfitting can be detrimental to the ...
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