False Negative

Definition of False Negative

False Negative: False negative is a result of a test where a condition that is true is incorrectly reported as being false.

What are the impacts of False Negatives?

False Negatives can have a significant impact and be costly to data science and machine learning projects. False Negatives refer to instances where the model predicts that something does not exist when in fact it does. This means that anything important that was predicted as non-existence, would be missed during the analysis, leading to incorrect conclusions and results. This can have a detrimental effect on data science and machine learning projects, potentially skewing results, hindering accuracy and invalidating the entire study or even lead to disastrous outcomes if used in a production environment.

For example, consider a model used for medical diagnosis which incorrectly predicts that a patient does not have a certain condition when they actually do. This has serious implications for both the patient’s health but also for any decisions taken based on the model’s output. In such cases, wrong decisions could lead to misdiagnosis or over/under treatment of patients which could have an extremely negative effect on their overall health.

False negatives can also have adverse effects on deep learning models where there is an imbalance between positive and negative classes of data samples. An imbalance occurs when one class of samples (for example those labeled ‘Positive’) are far more abundant than another (labeled ‘Negative’). When this occurs, there is an increased risk of false negatives due to the presence of too many positive samples which can make it difficult for the model to adequately learn from all available examples correctly. This leads to an increased rate of false negatives within the dataset which again has serious implications if used in production environments such as self driving cars or financial trading software among others.

Overall, false negatives are incredibly detrimental to data science and machine learning projects since they can throw off significant amounts of data leading researchers astray from their intended goals as well as causing problems if deployed at scale into production systems with real-world consequences. Therefore, it is highly recommended that researchers take extra care when training models so as to minimize errors from false negatives as much as possible.