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 key difference between a learned parameter and a hyperparameter?
B is correct. Coefficients, tree splits, and similar learned values come directly from minimizing a loss function on training data. Settings like max_depth or K are chosen beforehand and are never adjusted by the training process itself.
Question 02
Why does tuning hyperparameters by checking performance on the training set always favor more complex models?
B is correct. This is the same overfitting pattern from the previous lesson: training error keeps falling as complexity increases, so picking hyperparameters by training score alone reliably steers you toward an overfit model.
Question 03
When might random search be preferable to grid search?
B is correct. Grid search spends equal effort across every dimension of the grid, even unimportant ones. Random sampling, given the same budget, tends to explore the genuinely important hyperparameters more thoroughly.