Confusion Matrix — 5 Classes (CSV)
5×5 confusion matrix with off-diagonal noise for multi-class eval.
actual,P,Q,R,S,T
P,5,1,1,1,1
Q,1,5,1,1,1
R,1,1,5,1,1
S,1,1,1,5,1
T,1,1,1,1,5
Specifications
- Classes
- 5
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Confusion Matrix — 5 Classes (CSV)” 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: CSV · 83 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 pandas as pd
df = pd.read_csv("confusion-5class.csv")
print(df.head())
print(df.dtypes)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.
- jsonDetection Eval Metrics (JSON)Synthetic mAP evaluation summary for object-detection benchmark harness tests.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.