It is a test result which wrongly indicates that a particular condition or attribute is present.
Example – “Stress testing, a routine diagnostic tool used in detecting heart disease, results in a significant number of false positives in women”
In the context of machine learning, a false positive occurs when a model incorrectly predicts the positive class when the actual class is negative. In other words, the model erroneously indicates the presence of a condition or event that is not actually present.
For example, in a binary classification problem where the positive class represents a certain medical condition, a false positive would occur if the model incorrectly predicts that a person has the condition when, in reality, they do not.
The terminology is often associated with confusion matrices, where false positives are situated in the top-right quadrant, indicating instances where the model falsely predicted positive outcomes. The calculation of false positive rate (FPR) is given by:
FPR=(False Positives+True Negatives)/False Positives
Minimizing false positives is important in many applications, as it helps improve the precision of a model, which measures the accuracy of positive predictions.