{"uid":"cap_ssuz8yDaWec4glvcel_kL","slug":"return-reason-multiclass-log-loss-calculator-4c046bbb","name":"Return Reason Multiclass Log Loss Calculator","description":"Return reason - Multiclass log loss: Multiclass log loss for Return 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 multiclass log loss deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-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":["damaged","wrong_item","unwanted","other"],"type":"string"},"predicted":{"enum":["damaged","wrong_item","unwanted","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":"damaged sample document number 0 for this supplied dataset.","actual":"damaged","predicted":"damaged","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"damaged sample document number 1 for this supplied dataset.","actual":"damaged","predicted":"damaged","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"damaged sample document number 2 for this supplied dataset.","actual":"damaged","predicted":"damaged","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"wrong_item sample document number 0 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"wrong_item sample document number 1 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"wrong_item sample document number 2 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"unwanted sample document number 0 for this supplied dataset.","actual":"unwanted","predicted":"unwanted","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"unwanted sample document number 1 for this supplied dataset.","actual":"unwanted","predicted":"unwanted","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"unwanted sample document number 2 for this supplied dataset.","actual":"unwanted","predicted":"unwanted","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_xH1HEy3HLZVLHYgL0BVkY","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 return-reason text classification models across up to 200 labelled records","exampleAgentPrompt":"Can you compute the multiclass log loss for my return-reason classifier? I have 50 labelled records where each one has the actual label (damaged, wrong_item, unwanted, or other), the predicted label, and the 4-class probability vector my model output.","exampleUseCases":[{"title":"Validate return classifier before deployment","prompt":"Before I push my return reason model to production, can you run a multiclass log loss evaluation on these 120 labelled records? Each record has the customer return text, the actual reason (damaged, wrong_item, unwanted, or other), the model's predicted label, and the four class probabilities it output."},{"title":"Compare two model versions on return data","prompt":"I want to compare two versions of my return reason classifier — can you compute the multiclass log loss for version A's predictions? I have 80 records with the actual return reason labels and the predicted probabilities for each of the four classes: damaged, wrong_item, unwanted, and other."},{"title":"Audit naive-Bayes baseline for returns triage","prompt":"We're testing a naive-Bayes baseline for triaging customer returns. Can you calculate the multiclass log loss for 200 records where each has the true return reason label and the model's predicted probability distribution across damaged, wrong_item, unwanted, and other?"}],"resultDescription":"Returns a single bounded multiclass log loss value (cross-entropy) computed over all supplied labelled records, reflecting how well the predicted probability distributions match the actual return-reason classes (damaged, wrong_item, unwanted, other). Lower values indicate better-calibrated predictions.","failureModes":["More than 200 records supplied — request will be rejected or truncated","Probability vectors that do not sum to 1 or contain values outside [0,1]","Missing required fields (id, text, actual, predicted, probabilities) on any record","Probabilities array length not exactly 4","Invalid enum value for actual or predicted class","Empty records array"],"whenToPreferThis":"Choose this endpoint when you need a quick, cheap ($0.01 USDC) multiclass log loss evaluation specifically for return-reason text classification with exactly four classes (damaged, wrong_item, unwanted, other) on up to 200 records, without needing to host a model or use a foundation model. Best suited for local evaluation, model benchmarking, and naive-Bayes baseline assessment in e-commerce return workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:23.440Z","isFirstParty":false,"canonicalSlug":"return-reason-multiclass-log-loss-calculator-4c046bbb"}