Artificial intelligence · Grades 9–12
Examples, features, labels and data quality · revision notes
An example is one unit of observation. Features are inputs available at prediction time; a label is the target used for supervised learning.
Key ideas and conditions
Records
An example is one unit of observation. Features are inputs available at prediction time; a label is the target used for supervised learning.
Quality
Missing values, inconsistent units, duplicate records and ambiguous labels can distort learning. Cleaning choices need documentation.
Leakage
Information from the target or future must not enter features when it would be unavailable during actual prediction. Splits must respect linked records and time.
Privacy
Use authorized, appropriate data and minimize personal information. Synthetic examples in this course do not require uploading personal records.
Explain without looking
- Give two checks before using a dataset for an AI learning exercise.
- Should a missing numeric value always be replaced by zero without thinking?
Further reading and review references
These are reference links, not copied exercises, institutional endorsement or proof of independent review.
AI-assisted preview; independent educational review required. Level labels describe prerequisites, not accreditation or official curriculum alignment.