Practice

15 questions

• Classical ML

  What is overfitting? How would you detect it? And in a production forecasting system, how would you tell overfitting apart from temporal leakage?
  mid10 min

• Classical ML

  Your insurance-claims fraud classifier has 99.2% accuracy and 0.93 ROC-AUC. Fraud is 0.8% of claims. The investigation team can review 200 claims a week.
  mid8 min

• Classical ML

  A teammate adds a target-encoded travelagencyid (about 4,000 agencies, many with fewer than ten bookings) to a no-show model. Validation ROC-AUC jumps from 0.71 to 0.86.
  mid10 min

• Classical ML

  In a gradient-boosted model of booking cancellations, SHAP says "days to departure" is the top driver and that higher price lowers cancellation.
  senior12 min

• Classical ML

  A no-show model trained on a dataset where no-shows were undersampled to 50/50 is now used to compute expected no-shows per flight for overbooking.
  senior12 min

• Classical ML

  A bank's small-business credit team uses a logistic-regression scorecard. A new gradient-boosting model improves ROC-AUC from 0.74 to 0.79 on a holdout.
  staff25 min

• Classical ML

  A model predicts whether a customer will rebook within 90 days. The table has 10,000 rows from 2,000 customers (five bookings each, on average).
  mid8 min

• Classical ML

  A team tunes a gradient-boosting model with a random search of 200 configurations on one validation set and reports the best validation AUC (0.83) as the expected production performance.
  senior12 min

• Classical ML

  A lender's credit-decision model approves 30,000 of 100,000 applicants (hypothetical) and observes repayment only for approved loans, where 4.0% (1,200) default.
  staff25 min

• Classical ML

  You model ancillary revenue per booking with linear regression. Revenue is strongly right-skewed, so you fit it on the log scale and get a good fit.
  mid8 min

• Classical ML

  In a no-show model, the baggage-weight field is missing for 40% of high-risk-segment bookings and 5% of the rest (hypothetical). An analyst wants to drop rows with missing values;
  senior15 min

• Classical ML

  You fit a logistic regression for flight cancellations on bootstrap resamples of the same data. Two features, distancekm and scheduledminutes, correlate at 0.98.
  mid10 min

• Classical ML

  Marketing asks for "four customer segments" from a k-means model run on 120,000 loyalty members, using total spend (range 0 to 50,000), number of trips (0 to 30), days since last trip, and share of…
  senior15 min

• Classical ML

  A lender's credit model is retrained yearly on loans it approved. Leadership now wants to raise the approval rate from 40% to 60% of applicants and asks you to certify that the new model is safe.
  staff25 minin review

• Classical ML

  A teammate classifies 100 labelled claims as fraudulent or not using 2,000 candidate features extracted from claim text and metadata.
  mid10 min