EMERGING TRENDS IN COMPUTING
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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A model is overfit when it learns to classify the training samples so closely that it fails to make correct classification on the test samples.
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True
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False
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Either A or B
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None of the above
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Explanation:
Detailed explanation-1: -Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting.
Detailed explanation-2: -If we train for too long, the performance on the training dataset may continue to decrease because the model is overfitting and learning the irrelevant detail and noise in the training dataset. At the same time the error for the test set starts to rise again as the model’s ability to generalize decreases.
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