What is overfitting in machine learning?
a) When a model performs well on training data but poorly on unseen data
b) When a model performs well on both training and test data
c) When a model fails to learn from training data
d) When a model generalizes well to all datasets
Answer:
a) When a model performs well on training data but poorly on unseen data
Explanation:
Overfitting occurs when a machine learning model becomes too complex and learns the training data too well, including its noise and outliers. As a result, the model performs well on the training data but fails to generalize to new, unseen data.
This happens when the model captures patterns that are specific to the training data but are not representative of the underlying structure of the data. Overfitting can be avoided by using techniques such as regularization, cross-validation, and pruning.
A well-balanced model should generalize well to both the training and test data, ensuring that it captures the true patterns in the data rather than overfitting to irrelevant details.