You should understand the following terms of statistics to learn about the answer to your question.

**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.

Thus, for more study ROC Curve For Machine Learning

Hope this answer helps you!

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