Practice Overfitting (12.4.A) - Model Evaluation and Validation

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9 questions

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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 is overfitting?
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Think about the training accuracy versus test accuracy.
Question 2.
Name one technique to prevent overfitting.
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It adds a penalty to complexity.
3Question 3.
What is a sign of overfitting?
- High validation accuracy
- Low training accuracy
- High training accuracy and low validation accuracy
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Think about the difference between training and validation datasets.
4Question 4.
Is early stopping a method to prevent overfitting?
- True
- False
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Consider training dynamics.
Question 5.
Consider a dataset with numerous features. Describe a systematic approach to ensure your model does not overfit while tuning for the best performance.
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Think about balancing feature sets and evaluating performance.
Question 6.
After implementing a model with an accuracy drop on test data, what steps would you take to analyze and rectify the issue?
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Consider the possible reasons for discrepancies in performance.

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