There is no difference, they are the same, with the formula:

(true positive)/(true positive + false negative)

In the context of data analytics and machine learning, “true positive rate” (TPR) and “recall” are two terms often used interchangeably, but they represent slightly different concepts:

True Positive Rate (TPR):
- True Positive Rate is also known as Sensitivity or Recall.
- It measures the proportion of actual positive cases that are correctly identified by a classifier.
- Mathematically, TPR is calculated as: $TPR = T r u e P os i t i v es + F a l se N e g a t i v es T r u e P os i t i v es$
- It represents the ability of the model to correctly identify positive samples out of all actual positive samples.
Recall:
- Recall is a broader term that refers to the ability of a classifier to find all the relevant cases within a dataset.
- It’s the proportion of true positive cases that were correctly identified by the classifier, out of all actual positive cases.
- Mathematically, Recall is calculated as: $R ec a ll = T r u e P os i t i v es + F a l se N e g a t i v es T r u e P os i t i v es$
- Recall can be seen as a measure of a model’s completeness, indicating how many of the actual positive samples were retrieved by the model.

The key difference between TPR and recall lies in their conceptual interpretation. While they are mathematically equivalent, TPR specifically emphasizes the rate of correct identification of positive cases, whereas recall emphasizes the completeness of the model’s performance in capturing positive cases.

In summary, in an interview context, you can emphasize that TPR and recall are mathematically the same, but in practice, they may be interpreted slightly differently based on the emphasis on correct identification (TPR) versus completeness (recall) in the context of evaluating classifier performance.