{"uid":"cap_kYlB8e835ILgCjC_zJq0z","slug":"cancellation-reason-confusion-matrix-055ea529","name":"Cancellation Reason Confusion Matrix","description":"Cancellation reason - Confusion matrix: Confusion matrix 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 confusion matrix deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-cancellation-reason-confusion?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted"],"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"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"price sample document number 0 for this supplied dataset.","actual":"price","predicted":"price"},{"id":"example-0-1","text":"price sample document number 1 for this supplied dataset.","actual":"price","predicted":"price"},{"id":"example-0-2","text":"price sample document number 2 for this supplied dataset.","actual":"price","predicted":"price"},{"id":"example-1-0","text":"usage sample document number 0 for this supplied dataset.","actual":"usage","predicted":"usage"},{"id":"example-1-1","text":"usage sample document number 1 for this supplied dataset.","actual":"usage","predicted":"usage"},{"id":"example-1-2","text":"usage sample document number 2 for this supplied dataset.","actual":"usage","predicted":"usage"},{"id":"example-2-0","text":"quality sample document number 0 for this supplied dataset.","actual":"quality","predicted":"quality"},{"id":"example-2-1","text":"quality sample document number 1 for this supplied dataset.","actual":"quality","predicted":"quality"},{"id":"example-2-2","text":"quality sample document number 2 for this supplied dataset.","actual":"quality","predicted":"quality"},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other","predicted":"other"},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other","predicted":"other"},{"id":"example-3-2","text":"other sample document number 2 for this supplied dataset.","actual":"other","predicted":"other"}],"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_PrZ1M8z2-2hxkhXE7v-yc","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":"Computes a confusion matrix for a cancellation-reason classifier given up to 200 labelled text records with actual and predicted category labels.","exampleAgentPrompt":"Can you compute a confusion matrix for my cancellation reason classifier? I have 50 labelled records where each one has the customer text, the actual reason (price, usage, quality, or other), and what my model predicted — I want to see where it's getting confused.","exampleUseCases":[{"title":"Evaluate subscription churn classifier","prompt":"I built a model to predict why customers cancel — price, usage, quality, or other — and I have 120 labelled test records with actual and predicted labels. Can you run a confusion matrix so I can see where the model is making mistakes?"},{"title":"Spot price vs quality misclassification","prompt":"My cancellation reason classifier keeps mixing up 'price' and 'quality' complaints — can you generate a confusion matrix from these 80 labelled customer feedback records so I can quantify how often that's happening?"},{"title":"Baseline model sanity check","prompt":"Before I invest in a fancier model, I want to sanity-check my naive Bayes baseline on cancellation reasons. Here are 200 labelled records with actual and predicted categories across price, usage, quality, and other — please produce a confusion matrix."}],"resultDescription":"Returns a bounded confusion matrix deliverable showing the distribution of actual vs predicted cancellation reason categories (price, usage, quality, other), enabling identification of which category pairs are most commonly confused by the classifier.","failureModes":["Fewer than 2 records submitted — minimum record count not met","More than 200 records submitted — exceeds maximum batch size","Missing required fields (id, text, actual, predicted) in one or more records","Invalid enum value for actual or predicted (must be one of: price, usage, quality, other)","Record ID exceeds 80 characters or is empty","Payment not included or insufficient (requires 0.01 USDC via x402)"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-call confusion matrix evaluation for a cancellation-reason text classifier with up to 4 categories (price, usage, quality, other) and up to 200 records, without needing to host a model or set up a local evaluation pipeline. Ideal for quick model validation, baseline comparison, or spot-checking classifier outputs in a churn analysis workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:38.062Z","isFirstParty":false,"canonicalSlug":"cancellation-reason-confusion-matrix-055ea529"}