Protocol
Classification Losses and Decision Thresholds
Build a rare-event classifier on documented data or a synthetic scenario. Select a threshold before final evaluation and state which errors the user pays for.
35–60 active hours60–90 min sessionsIntermediate
Mastery contract
Core protocol · Standard 1.0 · Build a rare-event classifier on documented data or a synthetic scenario. Select a threshold before final evaluation and state which errors the user pays for.
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Reasoning — not yet passed
Derive logistic gradients and the cost-based threshold with explicit class and cost conventions.
Reliable implementation — not yet passed
NumPy gradients pass finite-difference checks below 1e-5 relative error on smooth fixtures.
Reproducible experiment — not yet passed
Thresholds are chosen on validation data and evaluated once on the holdout; baseline prevalence is reported.
Defense and handoff — not yet passed
Defend a threshold using decision costs and explain why accuracy alone can reward a useless classifier.
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 binary logistic loss and its gradient; derive a Bayes decision threshold for stated false-positive and false-negative costs.
Implement stable binary logistic regression without a classifier library and a softmax loss with shape checks; compare with a reference implementation.
Compare 5 validation-selected thresholds on an imbalanced problem; report confusion matrices, precision/recall and expected decision cost.
Debug reversed class labels and log(0) in cross-entropy; test extreme logits and all-negative predictions.
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'.