{"uid":"cap_I8mR4QPVZLT93owbBbNgn","slug":"cancellation-reason-label-and-prediction-audit-17aafc43","name":"Cancellation Reason Label and Prediction Audit","description":"Cancellation reason - Label and prediction audit: Label and prediction audit for Cancellation reason. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded label and prediction audit deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-cancellation-reason-label-audit?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted","probabilities"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["price","usage","quality","other"],"type":"string"},"predicted":{"enum":["price","usage","quality","other"],"type":"string"},"probabilities":{"type":"array","items":{"type":"number","default":0,"maximum":1,"minimum":0},"default":[],"maxItems":4,"minItems":4}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"price sample document number 0 for this supplied dataset.","actual":"price","predicted":"price","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"price sample document number 1 for this supplied dataset.","actual":"price","predicted":"price","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"price sample document number 2 for this supplied dataset.","actual":"price","predicted":"price","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"usage sample document number 0 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"usage sample document number 1 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"usage sample document number 2 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"quality sample document number 0 for this supplied dataset.","actual":"quality","predicted":"quality","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"quality sample document number 1 for this supplied dataset.","actual":"quality","predicted":"quality","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"quality sample document number 2 for this supplied dataset.","actual":"quality","predicted":"quality","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other","predicted":"other","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other","predicted":"other","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"other sample document number 2 for this supplied dataset.","actual":"other","predicted":"other","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]}],"maxItems":200,"minItems":2}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_bTUxOFFYci-wH66ghF3He","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Audits up to 200 labelled text records for a cancellation-reason classifier, computing evaluation metrics such as accuracy, confusion matrices, and log-loss against four classes: price, usage, quality, and other.","exampleAgentPrompt":"Can you audit my cancellation reason classifier results? I have 120 labelled records where each one has the actual label (price, usage, quality, or other), the predicted label, and the four class probabilities — run the label and prediction audit and give me the accuracy and confusion matrix breakdown.","exampleUseCases":[{"title":"Post-deployment classifier quality check","prompt":"I just deployed a cancellation reason model and want to audit its predictions on 80 labelled support tickets — each has the actual reason (price, usage, quality, or other), what the model predicted, and the probability scores. Can you run the label and prediction audit so I can see where it's going wrong?"},{"title":"Comparing model versions on held-out data","prompt":"I have 150 labelled cancellation records I held out for testing — actual labels are price, usage, quality, or other, and I've got the predicted labels and probability vectors from my new model. Run an audit so I can see the accuracy and per-class breakdown before I decide whether to ship this version."},{"title":"Debugging high-error cancellation category","prompt":"My cancellation classifier seems to be confusing 'price' and 'usage' reasons a lot. I have 60 labelled examples with actuals, predictions, and probabilities — can you do a label and prediction audit to confirm where the misclassifications are concentrated?"}],"resultDescription":"A bounded audit report covering aggregate and per-class evaluation metrics (accuracy, confusion matrix, log-loss, precision/recall) for up to 200 cancellation-reason records, comparing actual labels against predicted labels and probability outputs across the four classes: price, usage, quality, and other.","failureModes":["More than 200 records supplied — request exceeds the bounded input limit","Probability arrays not exactly length 4 — schema validation error","Invalid class label outside price/usage/quality/other enum — rejected record","Missing required fields (id, text, actual, predicted, probabilities) — validation failure","Malformed probability values outside [0,1] range — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost audit of an existing cancellation-reason classifier's label and prediction quality on a small labelled dataset (up to 200 records) with the four standard classes (price, usage, quality, other), without needing to train or host a model. It is best suited for spot-checking model performance, debugging misclassifications, or benchmarking against naive-Bayes baselines in a local evaluation workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T19:11:25.511Z","isFirstParty":false,"canonicalSlug":"cancellation-reason-label-and-prediction-audit-17aafc43"}