AI0-001 AI Concepts and Techniques Practice Question
A team is training a recurrent neural network (RNN) with LSTM units to predict stock prices. The validation loss is significantly higher than the training loss. Which action is MOST likely to reduce the gap?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the dropout rate in LSTM layers
A large gap between training and validation loss indicates overfitting. Increasing dropout (a regularisation technique) reduces overfitting by preventing co-adaptation of neurons. Increasing LSTM units or epochs would worsen overfitting, and reducing sequence length may lose important temporal patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of LSTM units
Why it's wrong here
Increasing model capacity would likely increase overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting, making the gap worse.
- ✗
Reduce the sequence length
Why it's wrong here
Reducing sequence length may lose important information and might not address overfitting.
- ✓
Increase the dropout rate in LSTM layers
Why this is correct
Dropout regularises the network, reducing overfitting and closing the train-validation gap.
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