The model learns noise instead of the underlying pattern and generalizes poorly to new data

The model is too basic to represent the relationships in the data and weak on all data

The model is trained with too many hyperparameters and thus becomes too slow

The model is too large and does not fit into the memory of the target hardware

Drift and model fit · Question 10 of 60

What is overfitting?

1 answer is correct. Easy

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Answer

C The model learns noise instead of the underlying pattern and generalizes poorly to new data

Explanation

An overfitted model has memorized the training data including its noise instead of learning the actual pattern. It therefore performs poorly on new data. If the model is too basic instead, this is called underfitting.

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