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Task Type Classifier — Macro, Micro & Weighted Scores
x402
Task Type Classifier — Macro, Micro & Weighted Scores is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes macro, micro, and weighted precision/recall/F1 aggregate scores for a task-type text classifier using up to 200 labelled records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Macro micro and weighted scores: Macro micro and weighted scores for Task type. 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 macro micro and weighted scores deliverable.
Output
Returns aggregate classification evaluation metrics — macro, micro, and weighted precision, recall, and F1 scores — computed over up to 200 supplied labelled text records with actual and predicted task-type labels (research, writing, analysis, implementation).
Example use cases
Benchmarking a new task classifier
“I just trained a naive Bayes model to classify tasks into research, writing, analysis, and implementation — can you run my 120 test records through the scorer and give me the macro, micro, and weighted F1 results?”
Comparing two model predictions
“I have 200 labelled task-type records and predictions from two different classifiers; can you compute the macro, micro, and weighted scores for prediction set A so I can compare it to set B?”
Validating classifier before production
“Before I deploy my task-type categorisation model, can you evaluate it on these 80 held-out labelled records — actual and predicted — and return the macro, micro, and weighted precision, recall, and F1 scores?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-aggregate-scores?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, low-cost aggregate classifier evaluation (macro, micro, weighted F1) for a four-class task-type text classifier (research, writing, analysis, implementation) without needing per-class confusion matrices or foundation-model inference. Ideal for iterative benchmarking of small local models on datasets up to 200 records.
Side effects
- More than 200 records supplied — batch will be rejected or truncated
- Missing required fields (id, text, actual, predicted) cause validation errors
- Invalid enum values for actual or predicted labels return schema errors
- Empty records array returns no meaningful scores
- Imbalanced classes may cause micro/macro divergence that looks surprising but is correct
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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