{"uid":"cap_RNRhbrnsl-LUqdwJTmqE9","slug":"classification-model-audit-ef52af42","name":"Classification Model Audit","description":"Classification Model Audit: Compute a confusion matrix, per-label precision/recall/F1, macro F1 and accuracy from up to 300 supplied expected/predicted labels. No model calls or claims about unseen data. 0.01 USDC includes up to 300 labelled predictions.","url":"https://market.datapackvibe.com/x402/assistant-classification-audit?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"examples":{"type":"array","items":{"type":"object","required":["expected","predicted"],"properties":{"expected":{"type":"string","maxLength":40,"minLength":1},"predicted":{"type":"string","maxLength":40,"minLength":1}},"additionalProperties":false},"default":[{"expected":"positive","predicted":"positive"},{"expected":"negative","predicted":"positive"}],"maxItems":300,"minItems":1}}},"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_8ciDXLl-_UQ_1Wsj-Fo86","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 confusion matrix, per-label precision/recall/F1, macro F1, and accuracy from up to 300 supplied expected/predicted label pairs","exampleAgentPrompt":"Can you audit these 200 classifier predictions for me? I have a list of expected vs predicted labels — I need the confusion matrix, per-label precision, recall, and F1, plus the overall macro F1 and accuracy.","exampleUseCases":[{"title":"Sentiment classifier performance review","prompt":"I ran my sentiment classifier on 150 test samples — each one has the true label (positive, negative, neutral) and what the model predicted. Can you compute the confusion matrix and per-label precision, recall, and F1 so I can see where it's going wrong?"},{"title":"Multi-class image tag audit","prompt":"I have 300 expected vs predicted category labels from my image tagger. Can you give me the full classification audit — confusion matrix, per-class F1, and the overall macro F1 and accuracy?"},{"title":"Spam detection model validation","prompt":"Before shipping my spam filter, I want to validate it on my held-out test set. I have 80 expected and predicted labels — can you calculate precision, recall, F1, and accuracy so I can confirm it meets our quality bar?"}],"resultDescription":"Returns a confusion matrix showing true/false positives and negatives per class, per-label precision, recall, and F1 scores, as well as overall macro F1 and accuracy — all computed from the supplied expected/predicted label pairs without making any model calls.","failureModes":["Empty or missing examples array returns a validation error","More than 300 label pairs submitted exceeds the limit and is rejected","Labels with empty strings or exceeding 40 characters fail schema validation","Mismatched or malformed JSON body returns a 400 error","Network or payment (x402) failure prevents the call from completing"],"whenToPreferThis":"Choose this endpoint when you have a fixed set of up to 300 ground-truth and predicted label pairs and need standard classification metrics (confusion matrix, per-label precision/recall/F1, macro F1, accuracy) computed quickly and cheaply without spinning up any model infrastructure. It is ideal for post-hoc model auditing, CI/CD quality gates, or reporting workflows where you already have predictions in hand.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T18:47:15.275Z","isFirstParty":false,"canonicalSlug":"classification-model-audit-ef52af42"}