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Listening Quiz: Thảo Luận Về Hiện Tượng Quá Khớp Và Chính Quy Hóa – Easy Level
Th4 6, 2025
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bởi
Anna Phạm
trong
Tiếng Anh dành cho Nhà khoa học dữ liệu (English for Data Scientist)
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1.
What is the main topic of the conversation?
A discussion about improving a model that is overfitting
A plan for collecting more test data
A conversation about changing the model’s input features
A debate about choosing the fastest training algorithm
Explanation:
Jessica and John discuss that the model is overfitting and suggest regularization methods to fix it.
2.
How does Jessica know the model is overfitting?
The weights are all set to zero
The test accuracy is higher than the training accuracy
The model is too slow to train
The training accuracy is high but the test accuracy is low
Explanation:
Jessica says the model is overfitting because the training accuracy is high but the test accuracy is low.
3.
Which regularization methods does Jessica suggest trying first?
Dropout and batch normalization
L1 and L2 regularization
PCA and clustering
Momentum and Adam
Explanation:
Jessica says, ‘We can try L1 or L2 regularization first.’
4.
What does L1 regularization add to the weights?
A noise term
An absolute value penalty
A constant learning rate
A squared value penalty
Explanation:
Jessica explains that L1 adds an absolute value penalty to weights.
5.
What can be inferred about the model after regularization is applied?
It is expected to overfit less by limiting the weights
It will always reach perfect training accuracy
It will no longer need test data
It will automatically become much larger
Explanation:
Jessica says both L1 and L2 help reduce overfitting by limiting weights, so the model is expected to overfit less.
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