A true negative is a prediction in a binary classification model where the model correctly identifies a data point as belonging to the negative class. This means the model predicted the data point would not exhibit the characteristic being analyzed, and this prediction was accurate.
Understanding True Negatives
Think of it like a medical test. A true negative is a result that correctly indicates a person does not have a certain disease.
Here's how it works in a more technical context:
- Binary Classification: This type of machine learning model categorizes data into two classes: positive and negative.
- True Negative: A true negative occurs when the model correctly predicts a data point as belonging to the negative class.
- False Negative: A false negative occurs when the model incorrectly predicts a data point as belonging to the negative class, when it actually belongs to the positive class.
- False Positive: A false positive occurs when the model incorrectly predicts a data point as belonging to the positive class, when it actually belongs to the negative class.
- True Positive: A true positive occurs when the model correctly predicts a data point as belonging to the positive class.
Examples of True Negatives
- Spam Detection: A spam filter correctly identifies an email as not being spam.
- Fraud Detection: A fraud detection system correctly identifies a transaction as not fraudulent.
- Medical Diagnosis: A medical test correctly identifies a patient as not having a specific disease.
Importance of True Negatives
Understanding true negatives is crucial for evaluating the performance of a classification model. They help determine the model's ability to accurately identify negative instances, which is essential in many applications.
