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Classification 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.

Model evaluation JSON parsing

Preview, first 34 linesjson

{
  "person": {
    "precision": 0.889,
    "recall": 0.923,
    "f1-score": 0.906,
    "support": 52
  },
  "car": {
    "precision": 0.927,
    "recall": 0.895,
    "f1-score": 0.911,
    "support": 57
  },
  "tree": {
    "precision": 0.936,
    "recall": 0.936,
    "f1-score": 0.936,
    "support": 47
  },
  "accuracy": 0.917,
  "macro avg": {
    "precision": 0.917,
    "recall": 0.918,
    "f1-score": 0.918,
    "support": 156
  },
  "weighted avg": {
    "precision": 0.917,
    "recall": 0.917,
    "f1-score": 0.917,
    "support": 156
  }
}

Specifications

Classes: 3
Metrics: precision, recall, f1-score, support
Accuracy: 0.917

Testing contract

Expected to pass

Scenario: Exercise Classification Report (JSON) in its eval workflow. A per-class classification report in the scikit-learn structure: precision, recall, F1, and support for each class plus accuracy and macro/weighted averages.
Expected result: top-level keys are person, car, tree, accuracy, macro avg, weighted avg; selected values: {"accuracy": 0.917}. Declared feature checks: classes=3; metrics=precision, recall, f1-score, support; accuracy=0.917.

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

“Classification Report (JSON)” is a deterministic Testaroo fixture for Model evaluation, JSON parsing. 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 · 588 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("classification-report.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/ai_datasets.py. Free for any use, no attribution required, license.