Activity: Create an ensemble comparison note

Estimated active time: 45-75 minutes

Scenario

Use the synthetic learner-support dataset. The fictional platform wants to decide which model families deserve a place in an algorithm portfolio.

Task

Fit bagging, a random forest and boosting, and report what each adds over a single tree and over each other.

Required sections

1. Problem and decision.
2. Validation score for a single tree, bagging, random forest, and boosting.
3. The gain each ensemble delivers over the single tree, stated as a difference.
4. Training time for each, alongside the score.
5. Keep, delay, or reject against the Module 1 baselines.
6. Limitation note.

Rules

• Include the single tree in the table. An ensemble's score means nothing without it.
• Report cost alongside accuracy — an ensemble that gains 0.005 for 40x the time is a poor trade.
• Use identical splits across all four, or the comparison is invalid.

Output

Use the relevant learner-resource template from learner-resources/ or write a one-page memo with the same sections.