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Unit 8 · Theory of Computation and Compilers

Overfitting is expected when we observe that?

Pick an option to check yourself, or open the answer below.

Show answer & explanation

Correct answer: B. With training iterations error on training set decreases but test set increases

Explanation

Overfitting means the model has started to memorise the noise and idiosyncrasies of the training data instead of learning the underlying pattern. Its signature during training is a training error that keeps falling while the test (validation) error begins to rise, producing the classic U-shaped validation curve whose minimum marks the right point to stop. Option (i) describes healthy learning, option (iii) indicates underfitting or a broken learning rate, and option (iv) means the model has stopped learning altogether. Hence option (ii) is correct.

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