What is overfitting in machine learning, and how can you prevent it from occurring in your models?
What is overfitting in machine learning, and how can you prevent it from occurring in your models?
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Overfitting is a common problem in machine learning, where a model is trained too well on the training data and ends up fitting the noise in the data instead of the underlying patterns. This can lead to poor performance on new, unseen data, which is the ultimate goal of a machine-learning model.
Overfitting occurs when a model is too complex relative to the amount of training data, and it can be detected when the model performs well on the training data but poorly on the test data. The model essentially memorizes the training data, rather than learning the general patterns that can be applied to new data.
There are several ways to prevent overfitting in machine learning: