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Meeting Topic Multiclass Brier Score Calculator
x402
Meeting Topic Multiclass Brier Score Calculator is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes the multiclass Brier score for a meeting-topic classifier across up to 200 labeled text records with four categories: planning, progress, decision, and review.
Price / call$0.01
Success—
StatusUnknown
How this service works
Meeting topic - Multiclass Brier score: Multiclass Brier score for Meeting topic. 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 multiclass brier score deliverable.
Output
Returns a multiclass Brier score (a scalar between 0 and 2, lower is better) summarizing the mean squared error between predicted class probabilities and one-hot encoded true labels across all submitted records and all four meeting topic classes (planning, progress, decision, review).
Example use cases
Validate meeting classifier before deployment
“I've built a meeting topic classifier that predicts planning, progress, decision, or review, and I have 120 labeled test records with both the actual labels and predicted probabilities. Can you run the multiclass Brier score on them so I can see how well-calibrated the model is before I ship it?”
Compare two meeting topic models
“I want to compare two versions of my meeting topic model — can you calculate the Brier score for version A first? I'll give you 80 records, each with the actual label, the predicted label, and the probability vector across planning, progress, decision, and review.”
Audit meeting classifier calibration quarterly
“We run a quarterly audit on our meeting categorization AI. Can you compute the multiclass Brier score on our latest 200 labeled meeting records? Each record has an ID, the meeting text snippet, the true category, the predicted category, and a probability array for planning, progress, decision, and review.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a probabilistic calibration metric (Brier score) specifically for a four-class meeting topic classifier (planning/progress/decision/review) on a small labeled dataset of up to 200 records. It is ideal for quick, cheap offline model evaluation without needing to host a model or call a foundation model API. Prefer it over accuracy-only metrics when you care about how well-calibrated the predicted probabilities are, not just whether the top predicted class is correct.
Side effects
- Probabilities array does not sum to approximately 1.0, causing an invalid input error
- Fewer than 1 or more than 200 records submitted, exceeding bounds
- Actual or predicted class values outside the allowed enum (planning, progress, decision, review)
- Record ID missing or exceeding 80-character limit
- Probabilities array does not contain exactly 4 elements
- Payment of 0.01 USDC not attached or x402 payment protocol not honored, resulting in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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