{"uid":"cap_VJ2fGNr1zDtCcFRIz7CZ9","slug":"meeting-topic-multiclass-log-loss-calculator-c65634e3","name":"Meeting Topic Multiclass Log Loss Calculator","description":"Meeting topic - Multiclass log loss: Multiclass log loss for Meeting topic. 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-meeting-topic-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":["planning","progress","decision","review"],"type":"string"},"predicted":{"enum":["planning","progress","decision","review"],"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":"planning sample document number 0 for this supplied dataset.","actual":"planning","predicted":"planning","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"planning sample document number 1 for this supplied dataset.","actual":"planning","predicted":"planning","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"planning sample document number 2 for this supplied dataset.","actual":"planning","predicted":"planning","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"progress sample document number 0 for this supplied dataset.","actual":"progress","predicted":"progress","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"progress sample document number 1 for this supplied dataset.","actual":"progress","predicted":"progress","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"progress sample document number 2 for this supplied dataset.","actual":"progress","predicted":"progress","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"decision sample document number 0 for this supplied dataset.","actual":"decision","predicted":"decision","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"decision sample document number 1 for this supplied dataset.","actual":"decision","predicted":"decision","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"decision sample document number 2 for this supplied dataset.","actual":"decision","predicted":"decision","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"review sample document number 0 for this supplied dataset.","actual":"review","predicted":"review","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"review sample document number 1 for this supplied dataset.","actual":"review","predicted":"review","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"review sample document number 2 for this supplied dataset.","actual":"review","predicted":"review","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_IZIPAcMGxSd23js7c41Bj","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 meeting topic classifier across up to 200 labelled text records with four topic categories.","exampleAgentPrompt":"Compute the multiclass log loss for my meeting topic classifier — I have 50 records where each has the actual label (one of planning, progress, decision, or review) and the predicted probabilities across all four classes. Here's my dataset.","exampleUseCases":[{"title":"Meeting classifier model benchmarking","prompt":"I trained a Naive Bayes classifier to label meeting segments as planning, progress, decision, or review. Can you calculate the multiclass log loss for these 80 labelled records, each with actual labels and predicted class probabilities?"},{"title":"Probability calibration check for meeting NLP","prompt":"I want to see how well-calibrated my meeting topic model's probability outputs are — here are 120 records with the actual meeting topic labels and the 4-class predicted probability arrays. Please compute the log loss."},{"title":"Comparing two meeting topic models","prompt":"I have two sets of predictions for the same 60 meeting transcript segments, both predicting across planning, progress, decision, and review. Can you compute the log loss for my first model's predictions so I can compare it against the second?"}],"resultDescription":"Returns a bounded multiclass log loss value computed over the supplied labelled records, reflecting how well the predicted probability distributions match the actual meeting topic labels across the four categories: planning, progress, decision, and review.","failureModes":["More than 200 records submitted — request exceeds the per-call limit","Probabilities array does not contain exactly 4 values per record — validation error","Probability values outside [0,1] range — schema validation failure","Invalid or unrecognised class label in actual or predicted fields — enum mismatch error","Empty records array submitted — no data to evaluate","Payment not processed — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need a quick, cheap ($0.01 USDC) multiclass log loss calculation for a meeting topic classifier with the specific four-class taxonomy (planning, progress, decision, review) and have up to 200 labelled records ready. It is ideal for lightweight model evaluation without needing to set up local ML tooling or pay for expensive hosted evaluation services. Prefer alternatives if you need per-class breakdown metrics, accuracy/F1, or support for more than 200 records at once.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:47:56.051Z","isFirstParty":false,"canonicalSlug":"meeting-topic-multiclass-log-loss-calculator-c65634e3"}