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Project Status Confusion Matrix
x402
Project Status Confusion Matrix is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Computes a confusion matrix for a multi-class project-status classifier (planned/active/blocked/complete) given up to 200 labelled and predicted text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project status - Confusion matrix: Confusion matrix 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 confusion matrix deliverable.
Output
Returns a bounded confusion matrix deliverable showing counts of correct and incorrect classifications across the four project status categories (planned, active, blocked, complete), enabling per-class accuracy, false-positive, and false-negative analysis for up to 200 submitted records.
Example use cases
Classifier accuracy audit for PM tool
“I just ran my project-status NLP model over 120 tickets and have both the actual and predicted labels for each. Can you build a confusion matrix so I can see how often it confuses 'blocked' with 'active'?”
Sprint planning model evaluation
“We trained a text classifier to predict whether a backlog item is planned, active, blocked, or complete. Here are 80 sample records with ground-truth and model predictions — give me the confusion matrix so the team can review precision and recall before we ship.”
Comparing two model versions
“I have 200 records with actual project statuses and predictions from our new classifier. Run the confusion matrix on them so I can compare where version 2 differs from version 1 in misclassifying 'complete' items.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-project-status-confusion?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap ($0.01 USDC) confusion matrix evaluation for a four-class project-status text classifier without needing hosted model inference or foundation-model training. It is ideal for local dataset evaluation, quick model diagnostics, and small-scale NLP benchmarking specifically within the planned/active/blocked/complete taxonomy.
Side effects
- More than 200 records submitted — only up to 200 are accepted
- Records missing required fields (id, text, actual, predicted) cause validation errors
- Invalid enum values for actual or predicted fields (outside planned/active/blocked/complete) are rejected
- Empty records array returns a degenerate or error response
- Record id exceeds 80 characters or text exceeds 1000 characters triggers schema violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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