{"uid":"cap_dmEM0u3F84ahH1nbnmtYM","slug":"calibration-court-isotonic-probability-calibration-5a56c8ca","name":"Calibration Court — Isotonic Probability Calibration","description":"Paid (x402, USDC on Base). Fit isotonic calibration on YOUR labelled outcomes ({p,y} pairs) and recalibrate the given probabilities. Statele","url":"https://mcp-x402.onrender.com/gate/calibration-court/api/v1/calibrate-labels?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"probs":{"type":"array"},"examples":{"type":"array"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","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":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_R7k3gjHBjbfsP3OfztXKB","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fits isotonic regression calibration on user-supplied labelled outcome pairs and recalibrates a set of raw probabilities using that fitted model.","exampleAgentPrompt":"I have a list of raw predicted probabilities from my classifier and the actual outcomes (0s and 1s) for a set of labelled examples — can you fit an isotonic calibration on those labelled pairs and then recalibrate my raw probability scores?","exampleUseCases":[{"title":"Classifier confidence score correction","prompt":"My fraud detection model outputs raw probabilities but they're poorly calibrated — I have 200 labelled examples with actual fraud outcomes. Can you fit isotonic calibration on those pairs and give me back corrected probabilities for my new batch of scores?"},{"title":"Medical risk model calibration","prompt":"I'm working with a clinical risk model that gives uncalibrated probabilities. I have ground-truth patient outcome labels paired with the model's predictions — please fit isotonic regression calibration on those and recalibrate these new probability estimates."},{"title":"Ad click-through prediction tuning","prompt":"Our ad CTR model's raw scores don't reflect real click rates. I have labelled {probability, actual_click} pairs from last month — can you fit isotonic calibration on them and recalibrate today's predicted click probabilities?"}],"resultDescription":"Returns recalibrated probability scores adjusted using an isotonic regression model fitted on the provided labelled outcome pairs. The output corrects systematic over- or under-confidence in raw probabilities so they better reflect empirical frequencies.","failureModes":["Malformed or empty probs/examples arrays return a 400 error","Non-monotone violations handled internally by isotonic regression pooling","Insufficient labelled examples may produce poorly fitted calibration","Payment not received or invalid USDC/x402 transaction returns 402 Payment Required","Server cold-start on Render may introduce latency on first call"],"whenToPreferThis":"Choose this endpoint when you have your own labelled ground-truth outcome pairs and need stateless, per-call isotonic probability calibration without maintaining server-side model state. Prefer it over Platt scaling when your calibration data may not follow a sigmoid shape, or when you need a non-parametric monotone correction. Ideal for agents that already have labelled validation sets and want a lightweight pay-per-call calibration step rather than integrating a full ML framework.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T12:35:16.351Z","isFirstParty":false,"canonicalSlug":"calibration-court-isotonic-probability-calibration-5a56c8ca"}