Question

What is the main difference between a false positive and a false negative in a

binary classification problem?
A. A false positive occurs when the model correctly classifies an instance as positive,
while a false negative occurs when the model incorrectly classifies an instance as
negative.
B. A false positive occurs when the model classifies an instance as negative, but it
actually belongs to the positive class, while a false negative occurs when the model
classifies an instance as positive, but it actually belongs to the negative class.
C. A false positive occurs when the model incorrectly classifies an instance as positive,
while a false negative occurs when the model correctly classifies an instance as
negative.
D. A false positive occurs when the model classifies an instance as positive, but it
actually belongs to the negative class, while a false negative occurs when the model
classifies an instance as negative, but it actually belongs to the positive class.

Answer 1

A false positive occurs when the model incorrectly classifies an instance as positive, while a false negative occurs when the model incorrectly classifies an instance as negative.

Step-by-step explanation:

The main difference between a false positive and a false negative in a binary classification problem is as follows:

A false positive occurs when the model incorrectly classifies an instance as positive, while a false negative occurs when the model incorrectly classifies an instance as negative.

For example, in a medical diagnosis scenario, a false positive would be when the model predicts a patient has a disease when they don't, while a false negative would be when the model predicts a patient doesn't have a disease when they actually do.

Some reasons for false positives can include noise or incorrect labeling of data, while false negatives can occur due to inadequate features or biases in the dataset.