Practice Overfitting And Underfitting (28.5) - Introduction to Model Evaluation

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10 questions

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6 sample questions

Question 1.
What does overfitting mean in machine learning?
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Think about the model's memory versus understanding.
Question 2.
What is underfitting?
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Consider how a model's complexity relates to its understanding of data.
3Question 3.
What is overfitting in machine learning?
- High accuracy on training data, low on test data
- Low accuracy on both, Good performance on training and test
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Focus on where the model excels and where it struggles.
4Question 4.
True or False: Underfitting can occur when a model is too complex.
- True
- False
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Revisit definitions of complexity in models.
Question 5.
Given a dataset with a linear trend, design a simplistic model for prediction. Indicate potential pitfalls of underfitting in your approach.
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What happens if too few features are included?
Question 6.
Explain how you would diagnose a model that is suspected of overfitting. What metrics and techniques would you use?
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Look at evaluation strategies for any discrepancies in model performance.

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