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
Why does KNN require feature scaling, while a decision tree generally doesn't?
B is correct. Distance formulas sum differences across all features, so a feature with a huge numeric range will dominate the calculation. Tree splits just compare a feature to a threshold, so the scale of the numbers doesn't change which split point is chosen.
Question 02
What happens as K increases in K-Nearest Neighbors?
B is correct. A larger K averages over more neighbors, smoothing out noise (lower variance) but eventually washing out genuine local structure (higher bias) if K gets too large.
Question 03
A decision tree's Gini impurity for a group is 0. What does this mean?
B is correct. Gini impurity measures the probability that two randomly chosen points from the group have different labels. If every point is the same class, that probability is 0.