What does overfitting mean in the context of machine learning?

What does overfitting mean in the context of machine learning?

• A. A model performs equally on training and unseen data
• B. A model memorizes the training set too well
• C. A model achieves high accuracy on all datasets
• D. A model ignores noise in training data

Answer: (B)

Overfitting in machine learning refers to a situation where a model learns the training data too well, capturing not only the underlying patterns but also the noise and outliers. This excessive focus on the training data can lead to poor generalization when the model is exposed to new, unseen data. When a model memorizes the training set too well, it achieves a very low error on this dataset but fails to perform accurately on new examples, as it has become tailored to the specific instances it was trained on. This results in the model being overly complex and sensitive to fluctuations in the training data. In contrast, a well-generalized model would perform well on both the training data and unseen data, indicating that it has learned the fundamental features rather than the specific details of the training samples.

Overfitting in machine learning refers to a situation where a model learns the training data too well, capturing not only the underlying patterns but also the noise and outliers. This excessive focus on the training data can lead to poor generalization when the model is exposed to new, unseen data.

When a model memorizes the training set too well, it achieves a very low error on this dataset but fails to perform accurately on new examples, as it has become tailored to the specific instances it was trained on. This results in the model being overly complex and sensitive to fluctuations in the training data. In contrast, a well-generalized model would perform well on both the training data and unseen data, indicating that it has learned the fundamental features rather than the specific details of the training samples.