You run 4-fold cross-validation. How many separate trainings happen, and what does each one use?

• 1 training on all 4 folds at once
• 4 trainings, each on 3 folds with the fourth fold as the check
• 4 trainings, each on all 4 folds at once with no fold held back
• 3 trainings, each on 1 fold with the other 3 as the check

You run 5-fold cross-validation. How many times is a model trained from scratch?

• 1 time
• 10 times
• 5 times

After cross-validation picks your model and settings, you should score the final untouched set once and report that number.

A dataset has 1000 rows. In 5-fold cross-validation, how many rows are in each validation fold?

A file's rows are sorted by customer region, so the rows of each of its several regions sit together, and there is no time order. Ravi puts the first quarter of the file in fold 1, the next quarter in fold 2, and so on. Each fold will still be representative of the whole dataset.

Monthly sales run from January 2019 to December 2024, and you want an honest estimate for 2025. Which split fits?

• Train on 2019 to 2023 and validate on 2024
• Shuffle every month into folds at random
• Train on 2024 alone and validate on 2019 to 2023
• Validate on every other month across the six years

Three penalty strengths are compared with 5-fold cross-validation. Their average validation scores are 0.81 (weak), 0.86 (medium) and 0.84 (strong), and their training-fold scores are 0.99, 0.90 and 0.85. Which penalty should you pick?

• Weak, because 0.99 is the best training-fold score
• Strong, because the simplest model is always safest
• Medium, because 0.86 is the best average validation score
• None of them, because the training and validation scores disagree

After picking a penalty by 5-fold cross-validation, Priya scores the untouched held-back set once, dislikes the number, changes the penalty, and scores the held-back set again. The second score is still an honest estimate of future performance.

You have hourly electricity readings for two years and want an honest estimate of next week's demand. You shuffle all the hours into 5 folds. What goes wrong?

• Nothing goes wrong, because shuffling is exactly what keeps every fold representative
• Five folds are too few for two years of hourly readings
• Hours from after each check period end up in training, so the model peeks ahead
• The readings must be split by day of the week instead

What is the correct split for a time-ordered dataset?

• Shuffle all rows and split at random
• Train on the newest rows, then validate on the oldest ones
• Train on the older rows, validate on the rows after them