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 a "hypothesis space"?
B is correct. Every algorithm restricts itself to a particular family of functions before it ever sees data. That family is the hypothesis space, and training searches within it for the best fit.
Question 02
A model that predicts whether an email is spam, trained on emails already labeled "spam" or "not spam," is an example of:
B is correct. The training data has labels (spam/not spam), and the label is a category, which makes this supervised classification.
Question 03
Why can't a linear regression model ever perfectly fit a strongly curved relationship, no matter how much data it gets?
B is correct. Linear regression can only express straight lines or flat hyperplanes. If the true relationship is curved, it simply isn't in the space of functions linear regression is allowed to search.