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Overfitting must be avoided to ensure model generalization from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Overfitting occurs when a model learns the training data too well, to the point that it starts to memorize the data rather than generalize from it. In other words, the model becomes too complex and starts to capture noise in the training data, rather than the underlying patterns. This can lead to poor performance when the model is applied to new, unseen data. To ensure that a model generalizes well to new data, it's important to avoid overfitting. One way to prevent overfitting is by using regularization techniques, which add a penalty to the model's complexity, discouraging it from fitting the noise in the training dat...
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