sklearn Classification Report (TXT)
sklearn-style classification report text for parser snapshot tests.
precision recall f1-score support
A 0.91 0.83 0.87 13
Specifications
- Format
- sklearn classification_report
Testing contract
Expected to pass- Scenario
- Exercise sklearn Classification Report (TXT) in its eval workflow. sklearn-style classification report text for parser snapshot tests.
- Expected result
- 3 text lines, decoded as UTF-8; first nonempty line is 'precision recall f1-score support'.
What is a .txt file?
TXT is a plain-text file containing unformatted character data with no styling or structure beyond line breaks. Its interpretation depends on character encoding, most commonly UTF-8, and on line-ending convention. It is the most universal and portable text container.
How to use this file
Use an example TXT to test encoding detection, line-ending (LF versus CRLF) handling, and any tool that reads or streams raw text input.
How to use this file for testing
“sklearn Classification Report (TXT)” 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: sklearn classification_report. 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.
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.