This quiz covers cross‑validation, regularisation, and diagnostics to prevent over‑fitting in MMM.
Over‑fitting occurs when a model captures ______ noise as signal.
random
systematic
cookie
baseline
Cross‑validation guards against over‑fitting by testing performance on ______ data.
same
identical
pixel
unseen
Regularisation techniques like Lasso add penalty proportional to absolute ______ size.
pixel
coefficient
cookie
font
Early stopping halts training when validation error ______.
is undefined
equals train error
hits zero
starts increasing
Bayesian MMM combats over‑fitting by using informative ______.
DNS
pixels
priors
cookies
Variance Inflation Factor alerts to multicollinearity, which can inflate coefficient variance and mimic ______.
LCP
pixel drops
over‑fitting
bot clicks
Model simplicity principle (Occam’s razor) recommends selecting the ______ model that explains data.
over‑parameterised
cookie best
simplest adequate
most complex
Drop‑one‑channel sensitivity tests remove each predictor to check for ______ dependence.
fragile
inverse
stable
cookie
Bootstrapped confidence intervals that widen dramatically relative to analytic ones hint at ______ instability.
pixel
fit
cookie
font
Robyn’s refit on synthetic test data (simulated) step measures whether model recovers known ______.
pixels
cookies
ground truth
font
Starter
Review the basics.
Solid
Nice work—refine the details.
Expert!
Exceptional command of the topic.
Mastering Model Over-Fitting Guards Interview Questions means learning to catch when your models start memorizing random noise instead of true signals. Sharpen your grasp by trying our Attribution & Marketing-Mix Modelling Interview Questions for a solid overview of credit allocation. Then tackle the dark social attribution practice MCQs to uncover hidden traffic patterns. Next, build on that with the CLV-weighted attribution interview questions to factor in customer value. Finally, round out your prep by exploring the retail foot traffic modelling interview resource and learn to handle real world data scenarios.
