{"uid":"cap_mNW7ZQZZN3NkFPwi0SuEM","slug":"product-category-fixed-holdout-learning-curve-a08179b9","name":"Product Category Fixed-Holdout Learning Curve","description":"Product category - Fixed-holdout learning curve: Fixed-holdout learning curve for Product category. 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 fixed-holdout learning curve deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-product-category-learning-curve?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["apparel","home","electronics","other"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"apparel sample document number 0 for this supplied dataset.","actual":"apparel"},{"id":"example-0-1","text":"apparel sample document number 1 for this supplied dataset.","actual":"apparel"},{"id":"example-0-2","text":"apparel sample document number 2 for this supplied dataset.","actual":"apparel"},{"id":"example-1-0","text":"home sample document number 0 for this supplied dataset.","actual":"home"},{"id":"example-1-1","text":"home sample document number 1 for this supplied dataset.","actual":"home"},{"id":"example-1-2","text":"home sample document number 2 for this supplied dataset.","actual":"home"},{"id":"example-2-0","text":"electronics sample document number 0 for this supplied dataset.","actual":"electronics"},{"id":"example-2-1","text":"electronics sample document number 1 for this supplied dataset.","actual":"electronics"},{"id":"example-2-2","text":"electronics sample document number 2 for this supplied dataset.","actual":"electronics"},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other"},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other"},{"id":"example-3-2","text":"other sample document number 2 for this supplied dataset.","actual":"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_MpHxjP3NESdr1PNzYtI_A","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":"Generates a fixed-holdout learning curve for product category text classification (apparel, home, electronics, other) using a naive-Bayes baseline on up to 200 labelled records.","exampleAgentPrompt":"Run a fixed-holdout learning curve on my labelled product descriptions — I have records tagged as apparel, home, electronics, or other — and show me how the naive-Bayes baseline accuracy changes as more training data is used. Here are my 80 labelled examples.","exampleUseCases":[{"title":"Benchmarking e-commerce classifier before scaling","prompt":"I have 120 labelled product descriptions split across apparel, home, electronics, and other — can you run a fixed-holdout learning curve so I can see how the naive-Bayes accuracy improves as training size increases, before I invest in a bigger model?"},{"title":"Estimating data needs for a product tagger","prompt":"We're building a product category tagger and only have 60 labelled text samples. Can you compute a learning curve using a fixed holdout so we can figure out roughly how many more labelled records we'd need to hit a decent accuracy?"},{"title":"QA check on a new product taxonomy dataset","prompt":"We just annotated 150 product listings as apparel, home, electronics, or other — run a fixed-holdout learning curve on them so we can sanity-check whether the labels are consistent and see how quickly the baseline improves."}],"resultDescription":"Returns a fixed-holdout learning curve report showing how a naive-Bayes baseline classifier's performance metrics (such as accuracy or F1) vary as the number of training records increases, evaluated against a held-out portion of the supplied labelled dataset spanning categories: apparel, home, electronics, and other.","failureModes":["Fewer than 2 records supplied — minimum input not met","Record text exceeds 1000-character limit — truncation or rejection","Invalid or missing 'actual' label not in allowed enum — validation error","Fewer than one example per class — class imbalance may prevent curve generation","Dataset too small to produce meaningful holdout split — degenerate curve output","Payment not processed — x402 payment required before response is returned"],"whenToPreferThis":"Choose this endpoint when you need a quick, inexpensive learning curve evaluation for product category text classification using a local naive-Bayes baseline, especially when you have a small labelled dataset (up to 200 records) and want to understand how model performance scales with training size — without deploying a hosted model or fine-tuning a foundation model. Ideal for early-stage data quality checks, dataset sizing decisions, and lightweight ML benchmarking on the four product categories: apparel, home, electronics, and other.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T00:54:05.412Z","isFirstParty":false,"canonicalSlug":"product-category-fixed-holdout-learning-curve-a08179b9"}