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Artificial intelligence · lesson 2 of 4

Examples, features, labels and data quality

An example is one unit of observation. Features are inputs available at prediction time; a label is the target used for supervised learning.

Grades 9–128 practice questionsNotes + worked examples
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Learn the ideas

Prerequisites: Tables, categories and averages.

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.

Worked examples

Example 1

In supervised learning, what is a label?

Labels specify what the model is trained to predict.

The target output for an example

Example 2

Which feature leaks future information when predicting tomorrow's sales today?

The final target would not be known at prediction time.

Tomorrow's final sales total

Check your understanding

8 questions

Decimals or fractions are accepted for numeric answers. Proofs and written responses use sample answers and require human marking. This is practice, not a protected examination.

1 In supervised learning, what is a label?

Show answer and explanation

The target output for an example

Labels specify what the model is trained to predict.

2 Which feature leaks future information when predicting tomorrow's sales today?

Show answer and explanation

Tomorrow's final sales total

The final target would not be known at prediction time.

3 A table has 200 records and 10 exact duplicates to remove. How many records remain?

Show answer and explanation

190

Subtract removed records from total records.

4 What should be recorded when converting metres to centimetres in a feature?

Show answer and explanation

The transformation and resulting units

Units and transformations affect interpretation and reproducibility.

5 Why can two rows from the same person in both train and test sets distort evaluation?

Sample answer and marking guidance

The model may exploit person-specific information shared across splits. Evaluation can become easier than generalizing to new people.

Credit linked records and a split matched to deployment.

6 Of 80 records, 20 lack a given feature. Find its missing fraction.

Show answer and explanation

0.25

20/80=0.25.

7 Give two checks before using a dataset for an AI learning exercise.

Sample answer and marking guidance

Check permission and privacy suitability, examine units and missingness, inspect label definitions, and define a leakage-safe split.

Credit any two concrete checks.

8 Should a missing numeric value always be replaced by zero without thinking?

Show answer and explanation

No

Zero can have a genuine meaning; missing-data handling must fit the task.

Further reading and review references

These are reference links, not copied exercises, institutional endorsement or proof of independent review.