ML 2.2Lesson outlineLesson 2 of 2 in Module 2
The Modelling Checks: Target, Leakage and Feature Decisions
Before modelling an unseen table, check the target, find the columns that must not be used, and write the decisions a modeller acts on.
Video in production.
Lesson outline: The Modelling Checks: Target, Leakage and Feature Decisions
In this lesson9 parts
- 01A column that should not be there
- 02The bank's calls
- 03Ranking columns by separation
- 04When each column becomes known
- 05Codes that mean something else
- 06Five indicators and the calendar
- 07Where these checks go wrong
- 08The code
- 09Recap, your challenge, and what comes next
Builds on
This lesson stands on its own. These go deeper into what it uses.
Key terms: The Modelling Checks: Target, Leakage and Feature Decisions
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- prediction time
- The moment the model is used: for the bank, before anyone dials.
- target leakage
- A feature carrying label information that is not available at prediction time:
duration. - data leakage
- Information in training that the model will not have when it is used; target leakage is one kind.
- sentinel value
- A special value that stands for a missing or unusual case, not an amount:
pdays = 999. - duplicate (reminder, ML 1.5)
- A row whose content repeats another row; the bank file has 12.
- data report
- The findings about a table, each with the decision it leads to: keep, transform, drop, or a question for a later video.
Quiz: The Modelling Checks: Target, Leakage and Feature Decisions
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.