What does 'overfitting' in model training mean?

What does 'overfitting' in model training mean?

• A. The model learns the training data too well
• B. The model performs well on new data
• C. The model is generalized but lacks detail
• D. The model is unable to learn from the training data

Answer: (A)

Overfitting occurs when a machine learning model learns the training data too well, to the extent that it captures noise and outliers in the data rather than just the underlying patterns. This results in a model that performs exceptionally well on the training dataset but poorly on unseen or new data. When a model overfits, it essentially memorizes the training examples instead of learning to generalize from them. This excessive learning can lead to high variance, where small changes in the input data can result in large changes in the model's output, making it less reliable in real-world applications. In contrast, a model that generalizes well strikes a balance—performing adequately on both the training and new datasets, indicating that it has effectively learned the patterns without being overly influenced by the peculiarities of the training data.

Overfitting occurs when a machine learning model learns the training data too well, to the extent that it captures noise and outliers in the data rather than just the underlying patterns. This results in a model that performs exceptionally well on the training dataset but poorly on unseen or new data.

When a model overfits, it essentially memorizes the training examples instead of learning to generalize from them. This excessive learning can lead to high variance, where small changes in the input data can result in large changes in the model's output, making it less reliable in real-world applications.

In contrast, a model that generalizes well strikes a balance—performing adequately on both the training and new datasets, indicating that it has effectively learned the patterns without being overly influenced by the peculiarities of the training data.