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.
{
"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
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.
Code examples
import json
with open("classification-report.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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- 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.
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- jsonlChat Fine-tuning Dataset — Anthropic Format (JSONL)The same synthetic conversations in the Anthropic Messages JSONL shape — a top-level system prompt plus a messages array of user and assistant turns. The format twin of the OpenAI file, for testing chat-format conversion.
- jsonlChat Fine-tuning Dataset — OpenAI Format (JSONL)A chat fine-tuning dataset in the OpenAI JSONL format — one conversation per line as a messages array with system, user, and assistant turns. Synthetic Q&A content. Paired with an Anthropic-format twin for testing format converters.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.