---
title: ROC Curve tools
description: The ROC Curve tools help you explore classification, performance, and statistics related to a selected model at any point on the probability scale.
---

# ROC Curve tools {: #roc-curve-tools }

The **ROC Curve** tab provides tools for exploring classification, performance, and statistics related to a selected model at any point on the probability scale. The following topics show how to use these tools:

| Topic | Describes... |
|---|---|
| [Use the ROC Curve tools](roc-curve-tab-use) | Accessing the ROC Curve tab and understanding its components. |
| [Select data and display threshold](threshold) | Setting the data source and display threshold used for ROC Curve visualizations. |
| [Confusion matrix](confusion-matrix) | Using a confusion matrix to evaluate model accuracy by comparing actual versus predicted values. |
| [Prediction Distribution graph](pred-dist-graph) | Viewing the distribution of actual values in relation to the display threshold. |
| [ROC curve](roc-curve) | Using a ROC curve to view a plot of the true positive rate against the false positive rate for given data source. |
| [Profit curve](profit-curve) | Generating a profit curve to estimate the business impact of a selected model. |
| [Cumulative charts](cumulative-charts) | Generating charts to help assess a model's cumulative characteristics. |
| [Custom charts](custom-charts) | Generating your own charts to  explore classification, performance, and statistics for a model. |
| [Metrics](metrics) | Viewing statistics that describe model performance at the selected display threshold. |


