Recall & Review
beginner
What is a residual in machine learning?
A residual is the difference between the actual value and the predicted value from a model. It shows how much the model's prediction misses the true value.
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beginner
Why do we analyze residuals after training a model?
We analyze residuals to check if the model fits the data well. Residual analysis helps find patterns that show if the model is biased or missing something important.
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intermediate
What does it mean if residuals show a clear pattern when plotted?
If residuals show a pattern, it means the model is not capturing some structure in the data. This suggests the model may be too simple or missing key features.
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intermediate
How can residual analysis help detect heteroscedasticity?
Heteroscedasticity means the residuals have changing spread or variance across predicted values. Residual plots can show this if residuals fan out or shrink as predictions change.
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beginner
What is the ideal distribution of residuals for a good regression model?
The ideal residuals are randomly scattered around zero with no clear pattern and have constant variance. They should look like random noise.
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What does a residual represent in a regression model?
If residuals form a clear curve pattern, what does it indicate?
What is heteroscedasticity in residual analysis?
Why should residuals be randomly scattered around zero?
Which plot is commonly used for residual analysis?
Explain what residual analysis is and why it is important in machine learning.
Describe how you would use a residual plot to check if a regression model is appropriate.