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 can't plain linear regression be used directly to predict a 0/1 class label?
B is correct. An unconstrained line will eventually predict values outside the 0-to-1 range for large or small enough feature values, which is meaningless as a probability.
Question 02
What does the sigmoid function do to the linear combination of features in logistic regression?
B is correct. The sigmoid function maps any real number to a value strictly between 0 and 1, which is exactly what's needed to turn an unbounded linear score into a valid probability.
Question 03
What happens if you lower the default 0.5 decision threshold?
B is correct. A lower threshold makes it easier for a predicted probability to count as "positive," which catches more true positives (higher recall) but also lets through more false positives (lower precision), the same tradeoff from the evaluation lessons.