Practice Overfitting (8.6.1) - Evaluation - CBSE 10 AI (Artificial Intelleigence)

Practice: Overfitting

Lesson 8.6.1Evaluation

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9 questions

Flashcard drill
4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does overfitting mean in the context of machine learning?
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Reflect on model performance discrepancies.
Question 2.
Name a technique that can help reduce overfitting.
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Think of ways to limit model complexity.
3Question 3.
What is the main characteristic of an overfitted model?
- High accuracy on unseen data
- High accuracy on training data but low on unseen data
- Consistent performance across all datasets
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Reflect on how accuracy varies between training and testing.
4Question 4.
True or False: Overfitting occurs when a model can successfully apply its learning to new data.
- True
- False
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Think about the concept of learning noise rather than true patterns.
Question 5.
You train a model with a high number of parameters on a small dataset. Explain the likely outcome in terms of overfitting and generalization.
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Consider how model capacity relates to data diversity.
Question 6.
Propose strategies to enhance a machine learning model suspected of overfitting after initial training results show a significant performance gap between training and validation datasets.
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Think about how each strategy prioritizes avoiding memorization of training data.

5 more questions in this section. Enroll free to practise them all.