{"uid":"cap_C_V27LqLQ5zewBKRWyo32","slug":"language-routing-per-label-precision-and-recall-fcb4c2d0","name":"Language Routing — Per-Label Precision and Recall","description":"Language routing - Per-label precision and recall: Per-label precision and recall for Language routing. 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 per-label precision and recall deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-language-routing-per-label?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["english","french","spanish","other"],"type":"string"},"predicted":{"enum":["english","french","spanish","other"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"english sample document number 0 for this supplied dataset.","actual":"english","predicted":"english"},{"id":"example-0-1","text":"english sample document number 1 for this supplied dataset.","actual":"english","predicted":"english"},{"id":"example-0-2","text":"english sample document number 2 for this supplied dataset.","actual":"english","predicted":"english"},{"id":"example-1-0","text":"french sample document number 0 for this supplied dataset.","actual":"french","predicted":"french"},{"id":"example-1-1","text":"french sample document number 1 for this supplied dataset.","actual":"french","predicted":"french"},{"id":"example-1-2","text":"french sample document number 2 for this supplied dataset.","actual":"french","predicted":"french"},{"id":"example-2-0","text":"spanish sample document number 0 for this supplied dataset.","actual":"spanish","predicted":"spanish"},{"id":"example-2-1","text":"spanish sample document number 1 for this supplied dataset.","actual":"spanish","predicted":"spanish"},{"id":"example-2-2","text":"spanish sample document number 2 for this supplied dataset.","actual":"spanish","predicted":"spanish"},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other","predicted":"other"},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other","predicted":"other"},{"id":"example-3-2","text":"other sample document number 2 for this supplied dataset.","actual":"other","predicted":"other"}],"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_3XP2OKrbD4cg1-EyKxJLO","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 per-label precision and recall metrics for a language routing classifier across English, French, Spanish, and Other categories using up to 200 labelled text records.","exampleAgentPrompt":"I have 150 labelled records from my language router — each has the actual language (english, french, spanish, or other) and what the model predicted. Can you compute the per-label precision and recall for each language class so I can see where it's making mistakes?","exampleUseCases":[{"title":"Diagnosing language router failure modes","prompt":"I've got 120 text samples with their true language labels and what my classifier predicted — can you break down precision and recall for each language (english, french, spanish, other) so I can see which class has the worst false-positive rate?"},{"title":"Validating a multilingual chatbot router","prompt":"Before deploying our multilingual support bot, I need to evaluate its language detection on 180 labelled test examples. Can you give me per-label precision and recall across english, french, spanish, and other so I can decide if it's ready?"},{"title":"Comparing model versions on language classification","prompt":"I trained two versions of our language router and I want to evaluate the newer one. Here are 95 records with actual and predicted labels — please compute precision and recall per language label so I can compare against the old model's scores."}],"resultDescription":"A per-label breakdown of precision and recall for each of the four language classes (english, french, spanish, other), computed from the supplied records containing actual and predicted labels. Allows identification of which language class the model performs best and worst on.","failureModes":["Records array empty or missing — returns error","Records exceed 200 item limit — returns validation error","Invalid enum value for actual or predicted field — returns schema error","Record ID missing or too long — returns validation error","Imbalanced dataset with zero examples for a class — may return undefined precision/recall for that label"],"whenToPreferThis":"Choose this endpoint when you need fine-grained, per-class evaluation of a language routing or language detection classifier across English, French, Spanish, and Other categories. It is ideal for debugging which specific language class is underperforming, rather than needing an aggregate accuracy score. Best suited for small offline evaluation batches (up to 200 records) using your own labelled data, with no need to host or train a model.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:47:36.954Z","isFirstParty":false,"canonicalSlug":"language-routing-per-label-precision-and-recall-fcb4c2d0"}