Test your ability to read and validate lift and cumulative gain charts. Learn what early decile concentration means for practical targeting.
A lift chart primarily shows ______.
feature importance for the top 10 variables
the absolute profit at each price point
the model’s calibration intercept and slope
how much better the model ranks positives than random across deciles
A steep cumulative gains curve in the first deciles means ______.
many positives are concentrated among the top-ranked cases
the model is perfectly calibrated
regularization strength is too high
there is class balance across all deciles
Baseline in lift/gain charts usually corresponds to ______.
random selection performance for the same population
the best achievable model on the dataset
training AUC multiplied by 100
a naive model predicting all zeros
If the model’s lift at top 10% is 3.0, this implies ______.
recall at 10% equals 30% exactly
AUC must be 0.90 or higher
targeting the top decile captures about three times more positives than random
precision is exactly 0.30 in that decile
Cumulative gain at 20% equals 60% means ______.
selecting the top 20% of cases captures about 60% of all positives
calibration slope is 0.60
the false positive rate is 0.40
the F1 score is 0.60
Lift and gains are most meaningful when ______.
tuning thresholds for perfectly balanced datasets only
measuring causal uplift in randomized experiments
estimating absolute revenue impact without cost info
evaluating rank-ordering for targeted actions under class imbalance
Why plot both train and test gains curves?
to guarantee monotonic feature effects
to compute a better baseline diagonal
to spot overfitting if train curves are strong but test curves collapse
to remove the need for cross-validation
When two models cross on a gains chart, a sensible next step is ______.
compare business metrics at actionable selection sizes
pick the model with the higher AUC regardless of segment
discard both models immediately
retrain using only the top decile data
Decile stability monitoring over time helps because ______.
it replaces the need for recalibration entirely
drift can erode early-decile lift even if AUC changes modestly
it makes class imbalance irrelevant
it only works for regression models
A perfect gains curve would ______.
imply zero false positives at all thresholds
jump to 100% of positives within the first small fraction of cases
track exactly along the diagonal baseline
show constant slope across all deciles
Starter
You’re getting the idea—revisit how baselines and deciles work.
Solid
Good ranking intuition—verify early‑decile lift on out‑of‑sample data.
Expert!
Superb—turn gains into business impact with cost‑aware selection.
