Cloudera
DS-200 · Question #53
Why should stop an interactive machine learning algorithm as soon as the performance of the model on a test set stops improving?
The correct answer is B. To prevent overfitting. See the full explanation below for the reasoning.
Question
Why should stop an interactive machine learning algorithm as soon as the performance of the model on a test set stops improving?
Options
- ATo avoid the need for cross-validating the model
- BTo prevent overfitting
- CTo increase the VC (VAPNIK-Chervonenkis) dimension for the model
- DTo keep the number of terms in the model as possible
- ETo maintain the highest VC (Vapnik-Chervonenkis) dimension for the model
How the community answered
(41 responses)- A5% (2)
- B78% (32)
- C2% (1)
- D12% (5)
- E2% (1)
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