{"uid":"cap_r6LGhqZMIyihFMcJbqPVL","slug":"document-type-multiclass-log-loss-calculator-01678ae4","name":"Document Type Multiclass Log Loss Calculator","description":"Document type - Multiclass log loss: Multiclass log loss for Document type. 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 multiclass log loss deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-document-type-log-loss?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":["invoice","report","correspondence","other"],"type":"string"},"predicted":{"enum":["invoice","report","correspondence","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":"invoice sample document number 0 for this supplied dataset.","actual":"invoice","predicted":"invoice","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"invoice sample document number 1 for this supplied dataset.","actual":"invoice","predicted":"invoice","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"invoice sample document number 2 for this supplied dataset.","actual":"invoice","predicted":"invoice","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"report sample document number 0 for this supplied dataset.","actual":"report","predicted":"report","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"report sample document number 1 for this supplied dataset.","actual":"report","predicted":"report","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"report sample document number 2 for this supplied dataset.","actual":"report","predicted":"report","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"correspondence sample document number 0 for this supplied dataset.","actual":"correspondence","predicted":"correspondence","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"correspondence sample document number 1 for this supplied dataset.","actual":"correspondence","predicted":"correspondence","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"correspondence sample document number 2 for this supplied dataset.","actual":"correspondence","predicted":"correspondence","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_b4nZr-GQoXIxGx0R_mzRH","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 multiclass log loss for a document-type classifier (invoice/report/correspondence/other) given up to 200 labelled text records with predicted probabilities.","exampleAgentPrompt":"Calculate the multiclass log loss for my document type classifier — I have 50 labelled records where each document is tagged as invoice, report, correspondence, or other, and I have the predicted probabilities for each class. Can you score the model's performance?","exampleUseCases":[{"title":"Audit document classifier calibration","prompt":"I built a classifier that sorts uploaded files into invoice, report, correspondence, or other. I have 120 labelled test records with the model's predicted probabilities for each class — can you compute the multiclass log loss so I know how well-calibrated it is?"},{"title":"Compare two document models","prompt":"I'm trying to decide between two document classification models. Can you compute the multiclass log loss for each using my 80 test records (invoice/report/correspondence/other) with their probability outputs so I can see which one performs better?"},{"title":"Validate classifier before production deploy","prompt":"Before I deploy my document-type classifier, I want to sanity-check it with log loss on 200 held-out labelled examples covering invoices, reports, correspondence, and other documents — can you run that evaluation for me?"}],"resultDescription":"Returns a bounded multiclass log loss value (cross-entropy loss) computed over up to 200 supplied text records, reflecting how well the classifier's predicted probabilities match the actual document type labels across the four classes: invoice, report, correspondence, and other. Lower values indicate better-calibrated predictions.","failureModes":["More than 200 records submitted — exceeds the maximum batch size limit","Probabilities array does not sum to 1.0 or contains values outside [0,1]","Probabilities array has fewer or more than 4 elements (must match the 4 classes)","Invalid or missing actual/predicted label values outside the allowed enum","Empty records array submitted with no data to evaluate","Malformed record objects missing required fields such as id, text, actual, predicted, or probabilities"],"whenToPreferThis":"Choose this endpoint when you need a quick, low-cost ($0.01 USDC) multiclass log loss evaluation for a document-type classifier specifically operating over the invoice/report/correspondence/other taxonomy, with up to 200 records per call. It is ideal for lightweight model evaluation, spot-checks, and benchmarking without needing a hosted model or full ML platform. Prefer it over general-purpose ML services when you already have predicted probabilities and ground truth labels and simply need the loss metric computed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:16.320Z","isFirstParty":false,"canonicalSlug":"document-type-multiclass-log-loss-calculator-01678ae4"}