What is “overfitting” in machine learning?
a) When a model performs very well on training data but poorly on test data
b) When a model performs equally well on training and test data
c) When a model is too simple and fails to capture patterns in the data
d) When a model performs well only on test data
Answer:
a) When a model performs very well on training data but poorly on test data
Explanation:
Overfitting occurs when a machine learning model becomes too complex and learns the training data, including noise and outliers. This results in excellent performance on training data but poor generalization to new, unseen data.
To combat overfitting, techniques like cross-validation, regularization, and pruning are used. These methods aim to simplify the model and make it more robust for unseen data.
A well-generalized model should balance complexity and accuracy, ensuring it performs well on both training and test data.