Protocol
Distance and Kernel Methods from Scratch
Choose a nearest-neighbor or kernel model for a small dataset with mixed feature scales. Defend the distance definition and show a failure case.
35–60 active hours60–90 min sessionsIntermediate
Mastery contract
Core protocol · Standard 1.0 · Choose a nearest-neighbor or kernel model for a small dataset with mixed feature scales. Defend the distance definition and show a failure case.
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Reasoning — not yet passed
Explain primal margin constraints and verify a polynomial kernel equals an explicit feature-space inner product.
Reliable implementation — not yet passed
Nearest-neighbor ties are deterministic and held-out examples never include themselves in the reference set.
Reproducible experiment — not yet passed
All comparisons share splits and include latency/memory as well as predictive metrics.
Defense and handoff — not yet passed
Defend a metric or kernel choice and explain why distance concentration can undermine the method.
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 a linear soft-margin objective and its constraints; demonstrate the kernel trick on a polynomial feature map.
Implement k-nearest neighbors and a linear hinge-loss classifier; compare an RBF SVM using the library solver without claiming to have written that solver.
Compare scaling/no-scaling and 3 distance or kernel settings on identical folds; report accuracy, query latency and Gram-matrix memory.
Debug self-neighbors leaking labels during validation and a nonsymmetric kernel matrix.
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'.