Q24ML Solution Monitoring, Maintenance, and SecurityMultiple answers
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs. Which solutions will mitigate this problem? (Choose two.)
Select 2 answers.
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A, B 7
Selected Answer: AB
The issue is overfitting. Soln:-
A. Early stopping:- Stops training once validation performance starts to decline
B. Increase dropout:- reduces overfitting by randomly turning off neurons
A, B 2
Selected Answer: AB
"improves substantially at first and then degrades after a specific number of epochs."
Clear sign to stop early and to drop
A, B 1
Enable early stopping to automatically halt model training when it at its best to prevent the model from overfitting and also adopting the dropout to remove specific neurons within the network thats are without a great impact on the model