Protocol
Trees and Ensembles with Honest Baselines
Create a tabular benchmark in which a simple model is a serious competitor. Explain any ensemble improvement using controlled capacity and feature comparisons.
35–60 active hours60–90 min sessionsIntermediate
Mastery contract
Core protocol · Standard 1.0 · Create a tabular benchmark in which a simple model is a serious competitor. Explain any ensemble improvement using controlled capacity and feature comparisons.
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Reasoning — not yet passed
Compute split gains correctly and explain bagging versus boosting with distinct mechanisms.
Reliable implementation — not yet passed
The tree handles constant features and empty candidate partitions without invalid leaves.
Reproducible experiment — not yet passed
Report all 4 model families on both tasks, including tuning effort, uncertainty and training cost.
Defense and handoff — not yet passed
Defend an ensemble choice and give a counterexample to interpreting impurity importance as causal influence.
All four criteria must pass. Activity completion does not satisfy them.
An independent reviewer reruns your work and varies something you did not rehearse: a fresh input, a different working directory, or a declared edge case.
Pilot decisions are provisional, not external credentials. For an appeal or sensitive artifact, contact your designated pilot operator with the submission ID. Export your assessment history.
What you will do
6 total
Derive squared-error and Gini split gains on a hand-sized dataset; explain why bagging primarily addresses variance.
Implement a shallow CART tree and bootstrap bagging in NumPy; compare against library trees before benchmarking random forests and gradient boosting.
Compare a linear baseline, tree, random forest and boosting on 2 tabular tasks with fixed folds and equal tuning budgets.
Debug a split that sends every sample to one child and a feature-importance conclusion driven by a high-cardinality identifier.
Write a 500–900 word technical report linking the reasoning, code, measurements, and limitations; include the project decision below.
Assess all 4 mastery criteria against saved artifacts, request an independent review, and repeat each failed criterion on a fresh example.
Essentials
A reading session alone never satisfies a mastery criterion.
Store source references, assumptions, and artifact paths beside every result.
Keep an untouched check case that differs from the worked example.
Record all attempts, including failures and results that contradict your prediction.
Use the stated comparison conditions; document every deviation before drawing a conclusion.
Protocol guardrails
Do
- +State the expected result before running the comparison.
- +Keep one minimal reproducible failing case when debugging.
- +Record environment versions and the exact command used.
- +Compare explanations with saved intermediate values.
- +Ask a reviewer to challenge the weakest assumption.
Don't
- ×Do not copy a worked solution and present it as an independent implementation.
- ×Do not tune against held-out evaluation outcomes.
- ×Do not report only the best seed or discard inconvenient runs.
- ×Do not equate elapsed hours or a completed run with a passed assessment.
- ×Do not conceal reduced-scale experiments behind claims about the original full-scale result.
Protocol authorship
Written by NuthinButta as instructional design. The teaching sources are the official references linked in each milestone; the exercises, workload and pass thresholds are ours, not their authors'.