# F1 Score vs ROC AUC

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I have the below F1 and AUC scores for 2 different cases

Model 1: Precision: 85.11 Recall: 99.04 F1: 91.55 AUC: 69.94

Model 2: Precision: 85.1 Recall: 98.73 F1: 91.41 AUC: 71.69

The main motive of my problem to predict the positive cases correctly, ie, reduce the False Negative cases (FN). Should I use the F1 score and choose Model 1 or use AUC and choose Model 2. Thanks

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## Sensitivity: If the model is 100% sensitive model, that means it didn’t miss any True Positive. Therefore, It predicted every value correct, means no False Negatives. But there is a risk of having a lot of False Positives.

## Specificity: Generally, if we have a 100% specific model, that means it did not miss any True Negative, in other words, there were no False Positives (i.e. negative result that is labeled as positive). But there is a risk of having a lot of False Negatives.

## Precision: Intuitively speaking, if we have a 100% precise model, that means it could catch all True positive but there were NO False Positive.

## Recall: Intuitively speaking, if we have a 100% recall model, that means it didn’t miss any True Positive, in other words, there were no False Negatives (i.e. a positive result that is labeled as negative).

## F1 Score

It's given by the following formula: F1 Score keeps a balance between Precision and Recall. We use it if there is uneven class distribution, as precision and recall may give misleading results.

## AUROC vs F1 Score (Conclusion)

In general, the ROC is used for many different levels of thresholds and thus it has many F score values. F1 score is applicable for any particular point on the ROC curve.

You may think of it as a measure of precision and recall at a particular threshold value whereas AUC is the area under the ROC curve. For F score to be high, both precision and recall should be high.

When you have a data imbalance between positive and negative samples, you should always use F1-score because of ROC averages over all possible thresholds.