Overfitting occurs when models memorize the training data from "summary" of Machine Learning by Ethem Alpaydin
Overfitting occurs when models memorize the training data. When we train a model, we aim to make it learn from the data so that it can generalize well to unseen instances. However, if a model memorizes the training data too well, it may not be able to generalize to new, unseen data. In other words, the model becomes too complex and captures noise in the training data as if it were a regular pattern. This leads to poor performance on new data because the model is essentially just regurgitating what it has seen before, rather than learning the underlying patterns in the data. One way to think about overfitting is like a student who memorizes the answers to specific practice problems without understanding the underlying concepts. When faced with a new, unseen problem, the student struggles because they have not truly learned the material - they have merely memorized specific instances. To prevent overfitting, we can use techniques like cross-validation, regularization, and early stopping. Cross-validation helps us evaluate the performance of our model on unseen data, regularization techniques like L1 and L2 help us simplify the model to prevent it from memorizing noise in the data, and early stopping helps us stop training the model when it starts to overfit the training data. By understanding and actively preventing overfitting, we can ensure that our models generalize well to new data and perform effectively in real-world applications.
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