We hope that you are already aware about below 4 terms:

- True Positive
- False Positive
- True Negative
- False Negative

In case you are not, then please read this short article on confusion matrix first and then proceed with this one.

There are few ratio related variables where we find out Positive or Negative Rate. Four metrics are designed for this. Let us check them out.

- True Positive Rate (TPR)
- False Negative Rate (FNR)
- True Negative Rate (TNR)
- False Positive Rate (FPR)

**1) True Positive Rate (TPR):**

**TPR = TP / P = 1 - FNR**

Here **P** stands for actual **Positive** class available in the data.

It simply means how many correct predictions happened for the **Positive** class divided by the count of actual **Positive** class. Or we can call it as percentage of actual **Positives** **correctly** predicted by the model.

**2) False Negative Rate (FNR):**

**FNR = FN / P = 1 - TPR**

It means that how many wrong predictions happened for the **Positive** class divided by the count of actual **Positive** class. Or we can call it as percentage of actual **Positives** **incorrectly** predicted by the model.

TPR + FNR = 100% of Actual Positives

**3) True Negative Rate (TNR):**

** TNR = TN / N = 1 - FPR**

Here **N** stands for actual **Negative** class available in the data.

It means how many correct predictions happened for the **Negative** class divided by the count of actual **Negative** class. Or we can call it as percentage of actual **Negatives** **correctly** predicted by the model.

**4) False Positive Rate (FPR):**

** FPR = FP / N = 1 - TNR**

It means that how many wrong predictions happened for the **Negative** class divided by the count of actual **Negative** class. Or we can call it as percentage of actual **Negatives** **incorrectly** predicted by the model.

TNR + FPR = 100% of Actual Negatives

Now you are almost done with this article, but wait!! We have not covered the topic for which this article is dedicated.

No worries.

You have already learnt the actual concept, now you just need to know which metric belongs to Sensitivity and which metric to Specificity. That’s it.

**Sensitivity or Recall:**

If you look at the definitions above, you may realize that the definition of **TPR** looks like the definition of **Recall** which we have studied already. The **True Positive Rate** is also known as **Sensitivity**.

**Specificity or TNR:**

The **True Negative Rate** is also known as **Specificity**.

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