Check your understanding of this module before moving on.
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Checkpoint quiz 3 questions
A quick check on this module's key ideas before you move on
Question 01
What is the cheapest first fix to try for imbalanced classification?
B is correct. class_weight="balanced" requires no new data and no changes to your dataset, just one extra constructor argument, making it the natural first thing to try.
Question 02
What is the main risk of random oversampling?
B is correct. Since oversampling copies existing minority rows exactly, the model can see the same minority examples many times, which risks memorizing them rather than generalizing.
Question 03
How does SMOTE differ from plain random oversampling?
B is correct. Interpolating between real minority points creates plausible new examples instead of exact duplicates, which reduces the overfitting risk that comes with simple duplication.