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4 cards from this section. Quick, good the night before a test.
What does MAE stand for?
Think about how we measure the average deviation of predictions.
Which metric squares the errors before averaging?
This metric gives higher penalties for larger errors.
What does R² Score indicate in regression analysis?
Think about how well the inputs account for outcomes.
True or False: RMSE is always less than MSE.
Consider the implications of square roots.
You have a regression model resulting in a high MAE and a low R² score. What steps would you take to improve this model?
Think about the quality of model fit and data used.
Given a dataset with known values and a predicted error of 10, how would one interpret an RMSE of 8?
Consider how RMSE reflects average prediction error.
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