{"uid":"cap_-MMenPXc7AJfbCKE7pm2i","slug":"project-status-fixed-holdout-learning-curve-67e2d130","name":"Project Status Fixed-Holdout Learning Curve","description":"Project status - Fixed-holdout learning curve: Fixed-holdout learning curve for Project status. 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-project-status-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":["planned","active","blocked","complete"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"planned sample document number 0 for this supplied dataset.","actual":"planned"},{"id":"example-0-1","text":"planned sample document number 1 for this supplied dataset.","actual":"planned"},{"id":"example-0-2","text":"planned sample document number 2 for this supplied dataset.","actual":"planned"},{"id":"example-1-0","text":"active sample document number 0 for this supplied dataset.","actual":"active"},{"id":"example-1-1","text":"active sample document number 1 for this supplied dataset.","actual":"active"},{"id":"example-1-2","text":"active sample document number 2 for this supplied dataset.","actual":"active"},{"id":"example-2-0","text":"blocked sample document number 0 for this supplied dataset.","actual":"blocked"},{"id":"example-2-1","text":"blocked sample document number 1 for this supplied dataset.","actual":"blocked"},{"id":"example-2-2","text":"blocked sample document number 2 for this supplied dataset.","actual":"blocked"},{"id":"example-3-0","text":"complete sample document number 0 for this supplied dataset.","actual":"complete"},{"id":"example-3-1","text":"complete sample document number 1 for this supplied dataset.","actual":"complete"},{"id":"example-3-2","text":"complete sample document number 2 for this supplied dataset.","actual":"complete"}],"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_ou9BxQL4u-t3o3kmnkWEW","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 a labelled project-status text dataset using a naive-Bayes classifier, showing how model accuracy changes with training set size.","exampleAgentPrompt":"Run a fixed-holdout learning curve on my project status dataset — I have about 80 labelled records where each entry has an ID, a short text description, and a label of either planned, active, blocked, or complete. I want to see how the naive-Bayes accuracy changes as training size grows.","exampleUseCases":[{"title":"Sample size sufficiency check for PM tool","prompt":"I have 120 labelled project update notes tagged as planned, active, blocked, or complete. Can you run a fixed-holdout learning curve on them so I can see whether collecting more labels would meaningfully improve classification accuracy?"},{"title":"Baseline model benchmark before scaling","prompt":"Before I invest in a larger model, I want to benchmark a naive-Bayes classifier on my 60 project status records — run a fixed-holdout learning curve and show me how accuracy evolves with training set size across the planned, active, blocked, and complete categories."},{"title":"Classroom exercise on learning curve shape","prompt":"I'm teaching a data science class and need a concrete learning curve example using project status text data. I have 40 labelled records split across planned, active, blocked, and complete — can you compute and return the fixed-holdout learning curve so students can see how accuracy climbs with more training examples?"}],"resultDescription":"Returns a fixed-holdout learning curve deliverable: a series of accuracy or performance metric values computed at increasing training set sizes using a naive-Bayes baseline on the supplied labelled text records, with the holdout evaluation set held constant throughout.","failureModes":["Fewer than 2 records supplied — minimum item count not met","Records missing required fields (id, text, actual) — validation error","Label values outside allowed enum (planned, active, blocked, complete) — schema rejection","More than 200 records supplied — exceeds maxItems limit","Text exceeds 1000-character limit per record — truncation or rejection","Insufficient class diversity for meaningful holdout split — degenerate curve output","Payment not completed via x402 protocol — request not processed"],"whenToPreferThis":"Choose this endpoint when you need a quick, inexpensive ($0.01 USDC) learning curve evaluation for project status text classification using a naive-Bayes baseline, with up to 200 labelled records and a fixed holdout methodology. Prefer it over hosted model solutions when you want a lightweight, local-style benchmark without training a foundation model, and when your labels are specifically project-status categories (planned, active, blocked, complete).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:29.441Z","isFirstParty":false,"canonicalSlug":"project-status-fixed-holdout-learning-curve-67e2d130"}