Overfitting is a concept in machine learning where a model learns the training data too well, to the point that it performs poorly on new, unseen data.
Imagine you’re studying for a history test by memorizing all the details in your textbook. You get really good at recalling the exact dates, names, and events from the book. But when you take the test, some questions ask about the general trends or the significance of the events, not the exact details you memorized. Even though you knew the textbook really well, you might do poorly on the test because you focused too much on the specific details and not enough on understanding the big picture. That’s a bit like overfitting.
In machine learning, a model is trained on a set of data called the training set. If the model is overfitting, it means it’s doing a great job at predicting the outcomes on the training set, but it’s not doing well on new, unseen data. This is because the model has learned the specific details and noise in the training set, but it hasn’t learned the underlying patterns that generalize to new data.
To avoid overfitting, we often use techniques like regularization, early stopping, or dropout, which all help the model to generalize better. We also use a separate validation set to monitor the model’s performance on unseen data during training.
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