ROC Curve Points (CSV)
An ROC curve as CSV: decision threshold with the corresponding false-positive and true-positive rates, monotonic from (0,0) to (1,1). A fixture for testing chart tools and AUC calculators.
threshold,fpr,tpr
1.0,0.0,0.0
0.9,0.02,0.35
0.8,0.05,0.55
0.7,0.08,0.68
0.6,0.12,0.78
0.5,0.18,0.85
0.4,0.26,0.9
0.3,0.37,0.94
0.2,0.52,0.97
0.1,0.71,0.99
0.0,1.0,1.0
Specifications
- Points
- 11
- Schema
- threshold, fpr, tpr
- Auc
- ≈0.93
Testing contract
Expected to pass- Scenario
- Exercise ROC Curve Points (CSV) in its eval workflow. An ROC curve as CSV: decision threshold with the corresponding false-positive and true-positive rates, monotonic from (0,0) to (1,1).
- Expected result
- 11 data records using ',' delimiters and UTF-8; header fields are threshold, fpr, tpr; data-record widths (columns:count) are {"3":11}. Declared feature checks: points=11; auc=≈0.93.
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
“ROC Curve Points (CSV)” is a deterministic Testaroo fixture for Model evaluation, CSV parsing, Time-series data. 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: schema: threshold, fpr, tpr. 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("roc-curve.csv")
print(df.head())
print(df.dtypes)Generated by generation/ai_datasets.py. Free for any use, no attribution required, license.
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