You work as a machine learning specialist for a personal care product manufacturer. You are creating a binary classification model that you want to use to predict whether a customer is likely to positively respond to toothbrush and toothpaste samples mailed to their house. Since your company incurs expenses for the products and the shipping when sending samples, you only want to send your samples to customers who, you believe, have a high probability of buying your products. When analyzing if a customer will follow up with a purchase, which outcome do you want to minimize in your confusion matrix to save costs?

Answer options:

A.True Negative
B.False Negative
C.False Affirmative
D.True Positive
E.False Positive

Answer: E
Option A is incorrect. True Negatives are definitely not an outcome you want to minimize because you definitely don’t want to send samples to customers who will not respond.
Option B is incorrect. You don’t need to limit False Negatives as much as false positives, since False Negatives only omit customers with a higher probability of following up. Not sending a sample to these customers won’t save costs.
Option C is incorrect. The terms used in a confusion matrix are: True Positive, False Negative, True Negative, and False Positive.
Option D is incorrect. True Positives are the ones to which you want to send your samples.
Option E is correct. You use a confusion matrix, or table, to describe the performance of a classification model on a set of test data when you know the true values. It’s called a confusion matrix because it shows when one class is mislabeled (or confused) as another. For example, when the observation is negative, the model prediction is positive (a False Positive). To reduce the number of mailings to customers who probably won’t follow up with a purchase, you want to limit False Positives.
Reference:
Please see the Wikipedia article titled Confusion Matrix.