AUC Calculator

Calculate Area Under the ROC Curve (AUC) from TPR/FPR values or CSV data for model evaluation.

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عن هذه الأداة

This AUC Calculator tool computes the Area Under the Receiver Operating Characteristic (ROC) Curve, a key metric for evaluating binary classification models in machine learning. Enter True Positive Rate (TPR) and False Positive Rate (FPR) values manually or upload a CSV file with your data. The tool calculates AUC using the trapezoidal rule, displays results with a visual ROC curve, and provides detailed output for analysis. Fully responsive and works entirely in your browser with no data sent to servers.

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كيفية الاستخدام

Choose input method: manually enter TPR/FPR values or upload a CSV file.
For manual entry: Add TPR and FPR values (between 0 and
in the table, with one pair per row.
For CSV upload: Ensure your file has columns labeled 'tpr' and 'fpr' with numeric values.
Click 'Calculate AUC' to compute the area under the curve.
View results including AUC value, ROC curve visualization, and data summary.

Frequently Asked Questions

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What is AUC and why is it important?

AUC (Area Under the ROC Curve) measures the performance of binary classification models. It represents the probability that the model ranks a random positive instance higher than a random negative instance, with values from 0 to 1 (higher is better).

What CSV format does the tool accept?

Upload a CSV file with two columns: 'tpr' (True Positive Rate) and 'fpr' (False Positive Rate). Values must be numeric between 0 and 1. The tool will ignore headers and parse all valid rows.

How is AUC calculated?

The tool uses the trapezoidal rule: AUC = Σ[(TPR_i + TPR_{i-1})/2 * (FPR_i - FPR_{i-1})] for sorted FPR values. This approximates the area under the ROC curve from your data points.

Can I use this tool without uploading data?

Yes, you can manually add TPR and FPR values using the table interface. Click 'Add Row' to insert new pairs, and enter values between 0 and 1 for each.

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