{"uid":"cap_d3OM-Zm5JRFne6tNvEsrK","slug":"training-question-multiclass-log-loss-calculator-e2fb1e37","name":"Training Question Multiclass Log Loss Calculator","description":"Training question - Multiclass log loss: Multiclass log loss for Training question. 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-training-question-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":["concept","procedure","example","assessment"],"type":"string"},"predicted":{"enum":["concept","procedure","example","assessment"],"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":"concept sample document number 0 for this supplied dataset.","actual":"concept","predicted":"concept","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"concept sample document number 1 for this supplied dataset.","actual":"concept","predicted":"concept","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"concept sample document number 2 for this supplied dataset.","actual":"concept","predicted":"concept","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"procedure sample document number 0 for this supplied dataset.","actual":"procedure","predicted":"procedure","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"procedure sample document number 1 for this supplied 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multiclass log loss for up to 200 labelled text records across four training question categories (concept, procedure, example, assessment)","exampleAgentPrompt":"Calculate the multiclass log loss for my training question classifier — here are 12 labelled records with actual and predicted classes (concept, procedure, example, assessment) and their probability vectors, and I need the bounded log loss score back.","exampleUseCases":[{"title":"Evaluate educational content classifier quality","prompt":"I trained a text classifier to sort study materials into concept, procedure, example, and assessment categories. I have 50 labelled records with the actual labels, my model's predicted labels, and the four-class probability outputs. Can you compute the multiclass log loss so I see how well-calibrated the predictions are?"},{"title":"Compare model iterations by log loss","prompt":"I have two batches of 80 predictions from two versions of my training question model — both with actual labels and probability scores across the four classes. Run the multiclass log loss calculation on this batch so I can compare which model version performs better."},{"title":"Validate naive Bayes baseline performance","prompt":"I built a naive Bayes baseline classifier for categorising training questions into concept, procedure, example, and assessment. Give me the multiclass log loss for these 30 test records using the actual labels, predicted labels, and probability arrays I'm providing."}],"resultDescription":"A bounded multiclass log loss value computed over the supplied labelled records. The response captures how well predicted probability distributions align with actual class labels across up to four categories (concept, procedure, example, assessment), returning a single deliverable numeric loss score.","failureModes":["More than 200 records submitted — request exceeds batch limit","Probability arrays do not contain exactly 4 values per record — schema validation failure","Probability values outside [0,1] range — rejected by schema constraints","Actual or predicted labels not in allowed enum (concept, procedure, example, assessment) — validation error","Empty records array — no computable loss","Record ID missing or exceeding 80 character limit — schema rejection","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need a fast, cheap ($0.01 USDC) multiclass log loss computation specifically for text classification tasks involving the training question taxonomy (concept, procedure, example, assessment). It is ideal for small local evaluations of up to 200 records without needing a hosted model or large foundation-model infrastructure. Prefer this over general ML platforms when you want a lightweight, per-call scoring primitive that fits into an agent workflow for iterative model evaluation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:43:38.136Z","isFirstParty":false,"canonicalSlug":"training-question-multiclass-log-loss-calculator-e2fb1e37"}