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?
Click on the arrows to vote for the correct answer
A. B. C. D. E.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.
To minimize costs, you would want to minimize the number of false positives in the confusion matrix when analyzing if a customer will follow up with a purchase.
A confusion matrix is a table that compares the predicted values of a model to the actual values. It is commonly used to evaluate the performance of classification models.
In a binary classification problem, the confusion matrix has four entries:
True Positive (TP): The model predicted a positive class and it was correct.
True Negative (TN): The model predicted a negative class and it was correct.
False Positive (FP): The model predicted a positive class, but it was incorrect (also known as a Type I error).
False Negative (FN): The model predicted a negative class, but it was incorrect (also known as a Type II error).
In the context of this problem, you want to minimize the number of false positives in the confusion matrix. False positives occur when the model predicts that a customer is likely to buy the product, but in reality, they are not. In this case, the company incurs unnecessary expenses for the products and shipping, and the customer may also feel inconvenienced or annoyed by receiving the samples that they have no interest in buying.
On the other hand, false negatives occur when the model predicts that a customer is not likely to buy the product, but in reality, they would have. In this case, the company misses an opportunity to gain a potential customer and make a sale.
However, in this problem, the goal is to minimize costs, and it is assumed that the cost of a false negative (missed opportunity) is less than the cost of a false positive (unnecessary expenses and potential customer dissatisfaction). Therefore, the correct answer is E. False Positive.