# Metrics
URL: https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/metrics-classic.html

> The Metrics pane in the DataRobot ROC Curve tab helps you explore statistics related to a selected machine learning model.

# Metrics

The Metrics pane, on the bottom right of the [ROC Curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-tab-use.html) tab, contains standard statistics that DataRobot provides to help describe model performance at the selected display threshold.

## View metrics

1. Select a model on the Leaderboard and navigate toEvaluate > ROC Curve.
2. Select adata sourceand set thedisplay threshold.
3. View theMetricspane on the bottom right: The Metrics pane initially displays the F1 Score, True Positive Rate (Sensitivity), and Positive Prediction Value (Precision). You can set up to six metrics.
4. To view different metrics, clickSelect metricsand select a new metric. NoteYou can select up to six metrics to display. If you change the selection, new metrics display the next time you access theROC Curvetab for any model until you change them again.

## Metrics explained

The following table provides a brief description of each statistic, using the detailed [classification use case](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-tab-use.html#classification-use-case-1) to illustrate.

| Statistic | Description | Sample (from use cases) | Calculation |
| --- | --- | --- | --- |
| F1 Score | A measure of the model's accuracy, computed based on precision and recall. | N/A |  |
| True Positive Rate (TPR) | Sensitivity or recall. The ratio of true positives (correctly predicted as positive) to all actual positives. | What percentage of diabetics did the model correctly identify as diabetics? |  |
| False Positive Rate (FPR) | Fallout. The ratio of false positives to all actual negatives. | What percentage of healthy patients did the model incorrectly identify as diabetics? |  |
| True Negative Rate (TNR) | Specificity. The ratio of true negatives (correctly predicted as negative) to all actual negatives. | What percentage of healthy patients did the model correctly predict as healthy? |  |
| Positive Predictive Value (PPV) | Precision. For all the positive predictions, the percentage of cases in which the model was correct. | What percentage of the model’s predicted diabetics are actually diabetic? |  |
| Negative Predictive Value (NPV) | For all the negative predictions, the percentage of cases in which the model was correct. | What percentage of the model’s predicted healthy patients are actually healthy? |  |
| Accuracy | The percentage of correctly classified instances. | What is the overall percentage of the time that the model makes a correct prediction? |  |
| Matthews Correlation Coefficient | Measure of model quality when the classes are of very different sizes (unbalanced). | N/A | formula |
| Average Profit | Estimates the business impact of a model. Displays the average profit based on the payoff matrix at the current display threshold. If a payoff matrix is not selected, displays N/A. | What is the business impact of readmitting a patient? | formula |
| Total Profit | Estimates the business impact of a model. Displays the total profit based on the payoff matrix at the current display threshold. If a payoff matrix is not selected, displays N/A. | What is the business impact of readmitting a patient? | formula |

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