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
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.
Code examples
import pandas as pd
df = pd.read_csv("roc-curve.csv")
print(df.head())
print(df.dtypes)Related files
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- csv10,000-Row CSVA CSV with 10,000 data rows — for testing streaming parsers, memory handling, and import performance.
- csvClean CSVA clean, well-formed CSV with a header and 20 rows — the baseline case for CSV parser testing.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.