A false negative in data science is when a model predicts a specific condition as negative or not present when it is usually positive or present. To be more precise, the model doesn’t find a positive example, so it comes up with a false negative outcome.
Impact of False Negatives
The impact of false negatives are:
1. Medical Diagnosis
False negatives can be especially troublesome in medical diagnosis uses, where a missed treatment can have serious consequences. For instance, if a medical imaging model fails to find a cancerous tumor, a patient’s therapies may be put off, worsening the patient’s condition.
2. Fraud Detection
False negatives can be an issue in fraud detection applications. This is when a model misses a fraudulent activity or transaction, causing a company or person to lose money.
3. Surveillance and Security
False negatives can be a problem in security and surveillance, where a model might miss something like suspicious behavior that could be a security threat.
4. Natural Language Processing
In natural language processing uses like text classification or sentiment analysis, false negatives can be a problem. If a model doesn’t find some phrases or words that are essential for putting text into categories, it could lead to erroneous results.
5. Quality Control
False negatives can be a concern in quality control. For example, a model might not find defective products or parts, which could cause safety or reliability problems.
