What changes outside this dataset?

Understand the idea

Population shift, group structure and changed feature availability can break apparent validation success. State the intended use and the new evidence needed rather than promising transportability.

Identify evidence needed for a changed use context.Scroll the diagram horizontally if needed.

What the syntax does

set(incoming.columns): Collects the available feature names into a set.
required - set(incoming.columns): Finds required names missing from the incoming table.
list: Turns the missing-name set into a list; its order does not affect this check.

Outputs you create

answer: The requested result, with the values, labels and row order described by this task.

LINE24 · first 8 rows
`distance`	`duration`
1	5.30501
1.47826	11.2361
1.95652	11.8488
2.43478	14.809
2.91304	10.7589
3.3913	19.4972
3.86957	17.89
4.34783	15.531

Your task

A fitted workflow needs distance and weight.
The incoming table has distance only.
Store the missing feature names in a list named answer.

answer

Hints

Think: Use sets to check an incoming schema before calling predict().

Tools: Use set(incoming.columns), required - set(incoming.columns), list. Read the visible syntax meanings before editing.

Approach: A fitted workflow needs distance and weight. The incoming table has distance only. Store the missing feature names in a list named answer. Keep the supplied row order and inspect the named output after running.

Explained solution

required = {'distance', 'weight'}
incoming = pd.DataFrame({'distance': [4, 7]})
answer = list(required - set(incoming.columns))

The incoming table is missing weight, so it cannot support this two-feature workflow as specified. Matching names is only the first check: units, availability at prediction time and the population must also match the intended use.