{"uid":"cap_ZEDVbexOlmv8B9gB7R0C0","slug":"review-aspect-multiclass-log-loss-calculator-29b85fbc","name":"Review Aspect Multiclass Log Loss Calculator","description":"Review aspect - Multiclass log loss: Multiclass log loss for Review aspect. 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-review-aspect-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":["performance","appearance","value","other"],"type":"string"},"predicted":{"enum":["performance","appearance","value","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":"performance sample document number 0 for this supplied dataset.","actual":"performance","predicted":"performance","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"performance sample document number 1 for this supplied dataset.","actual":"performance","predicted":"performance","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"performance sample document number 2 for this supplied dataset.","actual":"performance","predicted":"performance","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"appearance sample document number 0 for this supplied dataset.","actual":"appearance","predicted":"appearance","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"appearance sample document number 1 for this supplied dataset.","actual":"appearance","predicted":"appearance","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"appearance sample document number 2 for this supplied dataset.","actual":"appearance","predicted":"appearance","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"value sample document number 0 for this supplied dataset.","actual":"value","predicted":"value","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"value sample document number 1 for this supplied dataset.","actual":"value","predicted":"value","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"value sample document number 2 for this supplied dataset.","actual":"value","predicted":"value","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_fEQ7kOWvYXbdb0OG2p5f0","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 metric for review aspect classification (performance, appearance, value, other) from up to 200 labelled text records with predicted probabilities.","exampleAgentPrompt":"Can you calculate the multiclass log loss for my review aspect classifier? I have 50 labelled records where each review is tagged as performance, appearance, value, or other — I'll provide the actual labels, predicted labels, and the 4-class probability vectors for each record.","exampleUseCases":[{"title":"Evaluate product review classifier accuracy","prompt":"I trained a classifier to categorize product reviews into performance, appearance, value, or other. Can you compute the multiclass log loss using these 120 records with their actual labels and predicted probability distributions so I know how well calibrated the model is?"},{"title":"Compare naive Bayes baseline log loss","prompt":"I want to benchmark my naive Bayes review aspect model — can you run multiclass log loss on these 80 labelled review records with their actual and predicted aspect labels and 4-class probabilities to see how bad the baseline is?"},{"title":"Validate review aspect model before deployment","prompt":"Before we ship this review tagging model, can you calculate the log loss on this held-out test set of 150 review records, each with actual aspect labels, predicted labels, and probability scores across performance, appearance, value, and other?"}],"resultDescription":"Returns a bounded multiclass log loss scalar value derived from the supplied labelled records, quantifying the divergence between predicted class probabilities and actual labels across the four review aspect categories (performance, appearance, value, other).","failureModes":["More than 200 records supplied — request rejected or truncated","Probability vectors not summing to 1 or containing values outside [0,1]","Probability array length not exactly 4 — schema validation failure","Invalid or unrecognised class label in actual or predicted fields","Empty records array provided — no metric computable","Missing required fields (id, text, actual, predicted, probabilities) on any record"],"whenToPreferThis":"Choose this endpoint when you need a quick, cheap ($0.01 USDC) multiclass log loss calculation specifically for review aspect classification (performance, appearance, value, other) on small datasets up to 200 records, without setting up local ML libraries or hosting an evaluation service. Ideal for lightweight model benchmarking, sanity-checking classifiers, or comparing baseline models in a review-domain NLP pipeline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:47:51.245Z","isFirstParty":false,"canonicalSlug":"review-aspect-multiclass-log-loss-calculator-29b85fbc"}