Question

A model is built to determine whether data points belong to a category or not. A "true negative" result is:

a.A data point that is in the category, but the model incorrectly says it isn’t.
b. A data point that is not in the category, but the model incorrectly says it is.
c. A data point that is in the category, and the model correctly says it is.
d. A data point that is not in the category, and the model correctly says so.

Answer 1

A true negative result is a data point that is in the category, and the model correctly says it is.

Step-by-step explanation:

In the context of building a model to determine whether data points belong to a category or notb is option c. A data point that is in the category, and the model correctly says it is. This means that the model correctly identifies the data point as belonging to the category it actually belongs to.

For example, if we are building a model to predict whether an email is spam or not, a true negative would be a situation where a non-spam email is correctly identified as such by the model.