An engineering team is evaluating a binary classification model designed to flag fraudulent financial transactions. During testing, the model flags a completely legitimate purchase made by a user as "fraudulent," resulting in the user's card being temporarily blocked. What type of classification error does this event represent?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Alright, let's break this down. Think of it like this: your home security system starts screaming at 2 AM. You jump out of bed, grab a flashlight, and run downstairs only to find out... it was just your cat knocking over a plant. The alarm predicted an intruder (a positive event), but it was actually totally safe (a negative event). That's a false positive! In our credit card scenario, the model screams 'Fraud!' on a completely normal purchase. It's also known as a Type I error. It's super annoying for the customer, but it's different from a false negative, which is when the model sleeps right through an actual bad guy cleaning out your bank account. Got it? Sweet.
Full explanation below image
Full Explanation
In binary classification, model predictions are analyzed using a confusion matrix consisting of four categories: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). In this domain, the positive class is the target event we want to detect (fraudulent transaction), and the negative class is the normal state (legitimate transaction). - Option D (False positive) is correct because the model predicted the positive class (fraudulent) for an instance that is actually of the negative class (legitimate). This is also referred to as a Type I error. - Option A (False negative) is incorrect because a false negative occurs when the model predicts the negative class (legitimate) for an instance that is actually positive (fraudulent), representing a missed detection (Type II error). - Option B (True negative) is incorrect because a true negative occurs when the model correctly identifies a legitimate transaction as legitimate. - Option C (True positive) is incorrect because a true positive occurs when the model correctly identifies a fraudulent transaction as fraudulent.