Per-class Metrics (JSON)
Per-class precision/recall/F1 snapshot for classification dashboards.
{
"A": {
"precision": 0.91,
"recall": 0.83,
"f1": 0.87
}
}
Specifications
- Metrics
- precision, recall, f1
Testing contract
Expected to pass- Scenario
- Exercise Per-class Metrics (JSON) in its eval workflow. Per-class precision/recall/F1 snapshot for classification dashboards.
- Expected result
- top-level keys are A. Declared feature checks: metrics=precision, recall, f1.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Per-class Metrics (JSON)” is a deterministic Novus Examples fixture for Model evaluation. Benchmark results, confusion matrices, ROC curves, and classification reports in CSV and JSON, for testing eval dashboards, metric parsers, and leaderboard importers.
Documented properties for this file: JSON · 82 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
AI/ML fixtures are fully synthetic with documented schemas, no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.
Code examples
import json
with open("per-class-metrics.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonClassification Report (JSON)A per-class classification report in the scikit-learn structure — precision, recall, F1, and support for each class plus accuracy and macro/weighted averages. A fixture for testing metric parsers and report renderers.
- csvConfusion Matrix — 3 Classes (CSV)3×3 confusion matrix CSV for classification metric calculators.
- jsonConfusion Matrix — 3 Classes (JSON)JSON twin of the 3-class confusion matrix.
- csvConfusion Matrix — 3-class (CSV)A 3-class confusion matrix as CSV — rows are the true class, columns the predicted class, cells the counts. Paired with a JSON twin for testing metric parsers and evaluation visualisers.
- jsonConfusion Matrix — 3-class (JSON)The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.
- csvConfusion Matrix — 5 Classes (CSV)5×5 confusion matrix with off-diagonal noise for multi-class eval.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required, license.