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Machine Learning Metrics: Confusion Matrix
x402
Machine Learning Metrics: Confusion Matrix is a paid API for AI agents from ml-metrics.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-11-02).
Computes confusion matrix and derived classification metrics (accuracy, precision, recall, F1, MCC, Cohen's kappa, etc.) from true and predicted labels
Price / call$0.02
Success—
StatusUnknown
How this service works
Confusion matrix from true and predicted labels: accuracy, precision, recall, specificity, F1, MCC, kappa, averages.
Output
Returns a confusion matrix (2D array of counts), per-class and aggregate classification metrics including accuracy, precision, recall, specificity, F1, MCC, Cohen's kappa, and balanced accuracy. For binary problems, also returns TP, FP, TN, FN counts and the positive label used. Includes macro, micro, and weighted averages for multiclass scenarios.
Example use cases
Binary classifier evaluation after training
“I just finished training a spam detector — here are the actual labels and model predictions for my test set (both comma-separated, 500 values each). Can you compute the full confusion matrix and metrics like F1, precision, recall, and MCC, treating 'spam' as the positive class?”
Multiclass model performance report
“I have actual and predicted class labels for a 5-class image classifier across 2000 test samples. Can you give me the confusion matrix plus macro and weighted averages for precision, recall, and F1?”
Comparing two models head-to-head
“I want to compare two models on the same test set — can you compute confusion matrix metrics for each? First run: actual labels are 0,1,0,1,1,0 and model A predicted 0,1,1,1,0,0. Second run use the same actuals but model B predicted 1,1,0,1,1,0. Use 1 as the positive class for both.”
Technical details
Endpoint
GEThttps://ml-metrics.x402tookit.com/confusion-matrix?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a comprehensive set of classification metrics computed from raw true and predicted labels in a single call, especially when you need both binary-specific metrics (TP/FP/TN/FN, specificity, MCC) and multiclass averages (macro/micro/weighted) together. Prefer this over custom code when you want a fast, reliable, pay-per-use computation without spinning up a Python environment. Best suited for post-training evaluation pipelines or reporting tasks.
Side effects
- Mismatched lengths between actual and predicted arrays causes an error
- Invalid or non-JSON-parseable label strings rejected
- Empty label arrays return null or error
- Ambiguous positive class with more than 2 labels if not specified explicitly
- Very large arrays (>10000 values) may be rejected or truncated
- Non-string or mixed-type labels may cause parsing issues
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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