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confusion-cost board · triage clipboard with three lanes

Compare Classification Thresholds Without Hiding Error-Cost Tradeoffs

Two thresholds can trade false positives for false negatives while their accuracy percentages look reassuringly similar. Use a fixed classroom dataset and an explicit cost assumption to inspect that tradeoff instead of calling one threshold best without a criterion.

1 · Threshold code | true positives | false positives | false negatives | true negatives

FIRST-LOAD

HYPOTHESIS / PROTOTYPE — checkout unavailable. Calculation is local. A draft is saved automatically in this browser profile when storage is available; Reset to sample clears it. Optional Pro history stores only five summaries and has its own deletion control. State links encode your inputs and can remain in browser history, clipboard or recipients’ records; share only non-sensitive rows. Optional external AI formatting leaves this device. The required site analytics beacon reports page activity; shared URLs contain encoded inputs. Do not treat an encoded URL as private. The calculator has no input-collection endpoint.

Data note: The confusion-cost board processes Threshold code | true positives | false positives | false negatives | true negatives locally. Starter/sample selection and Run compute in this tab; no input is sent by the calculator. A local draft may be saved; explicit state-link sharing or optional external AI formatting can disclose inputs. Use non-sensitive labels.

Perspective: Before: one accuracy number concealed unlike error types. After: a fixed dataset and explicit error-cost multiple make the threshold tradeoff inspectable.

2 · Read the confusion-cost board

Classroom evaluation arithmetic only, not medical-test interpretation, automated hiring or a deployment recommendation. Ask an instructor or qualified analyst to review labels, evaluation design and cost assumptions before any consequential use.

Optional filing controls are a local prototype.

Checkout is unavailable. The reading above is complete; print, CSV and five local summaries are optional enhancements, not hidden answers.

Before using the confusion-cost board

Every row must evaluate the same labeled records under a different threshold. True positives plus false negatives must therefore be identical across rows, as must false positives plus true negatives. Both actual classes must be present. These checks cannot verify the labels or prevent training-data leakage; they only reconcile counts. A false positive costs one model unit and a false negative costs the supplied multiple. True outcomes carry zero model cost. Costs may be zero, hypothetical or unsuitable for a real application. The summary selects minimum cost per hundred records and exposes tied minima without claiming a universal optimum. This educational exercise is not for diagnosing patients, assigning rights or automatically deciding consequential outcomes.

Boundary and sources

Classroom evaluation arithmetic only, not medical-test interpretation, automated hiring or a deployment recommendation. Ask an instructor or qualified analyst to review labels, evaluation design and cost assumptions before any consequential use.

Mechanism: competence-autonomy-loop

Optional AI formatting, never the calculation

Manual entry completes this confusion-cost board for free without signup. If available to you, the free AI Studio interface linked in the sources may format fictional or non-sensitive notes; external access may require an account. No API key or AI call is built into this tool. Free-tier content may be used to improve products. Review each cell and transcribe it to the labeled row schema; do not paste the JSON object into the row box.

Format only these fictional or non-sensitive notes for a confusion-cost board. Return strict JSON shaped as {"rows": [{"label": "string", "cells": ["string", "string", "string", "string"]}], "setting": "string"}. The columns are Threshold code | true positives | false positives | false negatives | true negatives; the setting is False-negative cost in false-positive cost units. Keep all supplied strings and quantities exactly; do not calculate, infer missing entries, invent dates or add advice. If any required value is missing, return an empty rows array and ask me for it separately. I will verify every cell against my source and manually transcribe rows using vertical bars before running the local calculator.

An AI response is not executed, fetched or trusted as a result. Missing values remain questions; the strict local parser checks the rows you actually enter.