What are false positives and false negatives

False positives and false negatives are terms commonly used in binary classification tasks, such as spam detection, medical diagnosis, and anomaly detection. They represent different types of errors that can occur when making predictions or decisions.

1. False Positive:

   • In a binary classification task, a false positive occurs when the model incorrectly predicts the positive class (also known as the "1" class or the presence of something) when the actual class is negative (the "0" class or the absence of something).
   • False positives are also known as Type I errors or false alarms.
   • For example, in a medical test for a disease, a false positive result would indicate that the test incorrectly identifies a healthy person as having the disease.

2. False Negative:

   • In contrast, a false negative occurs when the model incorrectly predicts the negative class (the absence of something) when the actual class is positive (the presence of something).
   • False negatives are also known as Type II errors or misses.
   • Using the same medical test example, a false negative result would mean that the test fails to detect the disease in a person who actually has it, incorrectly classifying them as healthy.

In summary:

• False Positive: Predicting a positive outcome when the actual outcome is negative.
• False Negative: Predicting a negative outcome when the actual outcome is positive.

Both false positives and false negatives can have significant consequences depending on the specific application. The goal of a classification model is typically to minimize both types of errors, but there is often a trade-off between them. For example, in medical testing, reducing false negatives (missing actual cases of a disease) may be more critical than reducing false positives (incorrectly diagnosing healthy individuals). Therefore, the choice of evaluation metrics and optimization strategies depends on the relative costs and benefits associated with false positives and false negatives in a particular domain.