{"uid":"cap_t3Jys_UyJsvTcitmvdYr8","slug":"return-reason-multiclass-brier-score-calculator-e6caa87c","name":"Return Reason Multiclass Brier Score Calculator","description":"Return reason - Multiclass Brier score: Multiclass Brier score 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 brier score deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-brier?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_3StRuGDi3lHXGrNa6q4Oe","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 the multiclass Brier score for a return-reason classifier given up to 200 labelled text records with predicted probabilities.","exampleAgentPrompt":"Calculate the multiclass Brier score for my return-reason classifier — I have 50 labelled records where each record has the actual class (damaged, wrong_item, unwanted, or other), my model's predicted class, and a 4-element probability vector; can you run this evaluation and tell me how well my probabilities are calibrated?","exampleUseCases":[{"title":"Calibration check after model retrain","prompt":"I just retrained my return-reason classifier and I have 150 labelled validation records with actual labels across damaged, wrong_item, unwanted, and other, plus the 4-class probability outputs. Can you compute the multiclass Brier score so I can compare calibration before and after retraining?"},{"title":"A/B comparison of two classifiers","prompt":"I'm comparing two models that predict return reasons. I have 80 test records with actual labels and probability vectors from model A — please compute the multiclass Brier score for model A so I can benchmark it against model B."},{"title":"Quality gate before production deploy","prompt":"Before we ship this return-reason model to production, run a multiclass Brier score on these 200 labelled records with the model's predicted probabilities across damaged, wrong_item, unwanted, and other — flag it if the score looks too high to deploy."}],"resultDescription":"Returns a single multiclass Brier score (a float between 0 and 2, where 0 is perfect) summarising the mean squared error between predicted probability vectors and one-hot actual labels across all supplied records and the four return-reason classes (damaged, wrong_item, unwanted, other).","failureModes":["More than 200 records supplied — request rejected or truncated","Probability vectors that do not sum to 1 or contain values outside [0,1] — validation error","Mismatched enum value in actual/predicted fields — schema validation error","Empty records array — returns error or degenerate score","Fewer than 4 probabilities in a probability vector — schema error"],"whenToPreferThis":"Choose this endpoint when you need a cheap, fast, bounded multiclass Brier score specifically for the four return-reason classes (damaged, wrong_item, unwanted, other) without standing up your own evaluation infrastructure. It is ideal for quick calibration checks, CI/CD quality gates, or comparing model versions on small batches of up to 200 records. It is not suitable for large-scale offline batch evaluation, arbitrary class taxonomies, or use cases requiring full confusion matrices or other metrics beyond the Brier score.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:32.926Z","isFirstParty":false,"canonicalSlug":"return-reason-multiclass-brier-score-calculator-e6caa87c"}