{"uid":"cap_md4JDXxY3kImomK8N0Co6","slug":"review-aspect-confusion-matrix-8a5aad0a","name":"Review Aspect Confusion Matrix","description":"Review aspect - Confusion matrix: Confusion matrix for Review aspect. 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 confusion matrix deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-confusion?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":["performance","appearance","value","other"],"type":"string"},"predicted":{"enum":["performance","appearance","value","other"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"performance sample document number 0 for this supplied dataset.","actual":"performance","predicted":"performance"},{"id":"example-0-1","text":"performance sample document number 1 for this supplied dataset.","actual":"performance","predicted":"performance"},{"id":"example-0-2","text":"performance sample document number 2 for this supplied dataset.","actual":"performance","predicted":"performance"},{"id":"example-1-0","text":"appearance sample document number 0 for this supplied dataset.","actual":"appearance","predicted":"appearance"},{"id":"example-1-1","text":"appearance sample document number 1 for this supplied dataset.","actual":"appearance","predicted":"appearance"},{"id":"example-1-2","text":"appearance sample document number 2 for this supplied dataset.","actual":"appearance","predicted":"appearance"},{"id":"example-2-0","text":"value sample document number 0 for this supplied dataset.","actual":"value","predicted":"value"},{"id":"example-2-1","text":"value sample document number 1 for this supplied dataset.","actual":"value","predicted":"value"},{"id":"example-2-2","text":"value sample document number 2 for this supplied dataset.","actual":"value","predicted":"value"},{"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_B70YxoQKv9LkH9skpSa_E","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 a confusion matrix for a review aspect classifier (performance, appearance, value, other) given up to 200 labelled text records with actual and predicted labels.","exampleAgentPrompt":"I have up to 200 labelled product reviews where each record has an actual aspect label (performance, appearance, value, or other) and a predicted label from my classifier — can you compute the confusion matrix for those results?","exampleUseCases":[{"title":"Evaluating review classifier model accuracy","prompt":"I trained a model to classify product reviews into performance, appearance, value, or other aspects. I have 150 records with actual and predicted labels — can you run a confusion matrix so I can see where my classifier is getting things wrong?"},{"title":"Diagnosing misclassification between aspect categories","prompt":"My review aspect classifier keeps mixing up 'value' and 'performance' reviews. Here are 80 labelled examples with actual and predicted labels — can you generate a confusion matrix so I can see the exact breakdown of misclassifications?"},{"title":"Baseline NLP model evaluation before deployment","prompt":"Before I deploy my review aspect tagger, I want to evaluate it on 120 held-out labelled reviews with actual labels (performance, appearance, value, other) and my model's predictions. Can you produce a confusion matrix for this set?"}],"resultDescription":"A bounded confusion matrix deliverable showing, for each combination of actual and predicted review aspect labels (performance, appearance, value, other), how many records fall into each cell — enabling calculation of per-class precision, recall, and overall accuracy from the supplied labelled records.","failureModes":["Exceeding 200 records returns an error or truncation","Missing required fields (id, text, actual, predicted) cause validation failure","Invalid enum values for actual or predicted labels (outside performance/appearance/value/other) cause rejection","Empty records array returns no meaningful matrix","Highly imbalanced label distributions may produce degenerate confusion matrices"],"whenToPreferThis":"Choose this endpoint when you need a quick, low-cost confusion matrix for a small review aspect classification dataset (up to 200 records) with four fixed categories (performance, appearance, value, other), and you already have both actual and predicted labels. It is ideal for lightweight model evaluation without needing a hosted ML platform or foundation model inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T00:53:41.010Z","isFirstParty":false,"canonicalSlug":"review-aspect-confusion-matrix-8a5aad0a"}