The true negative (TN), together with true positive (TP), is a type of accurately predicted outcome, meaning that the prediction matches the true observation/ condition in terms of the class (positive/ negative).  
 
TN occurs when a negative class prediction (classification) was accurately made.  




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- True Positive (TP)
- True Negative (TN)
- False Positive (FP)
- False Negative (FN)

Components of Confusion Matrix

https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative



Classification: True vs. False and Positive vs. Negative

The true positive (TP), together with true negative (TN) (*) is a type of accurately predicted outcome, meaning that the prediction matches the true observation/ condition in terms of the class (positive/ negative).  
 
TP occurs when a positive class prediction (classification) was accurately made.  


True Positive (TP)

True Negative (TN) 

The false positive (FP), together with false negative (FN) (*), is a type of inaccurately predicted outcome, meaning that the prediction does not match the true observation/ condition in terms of the class (positive/ negative).  
 
FP occurs when a positive class prediction (classification) was inaccurately made when a negative class prediction, when accurately predicted, should have been made.  

In the example of the mammogram: 
If the woman who is tested positive for breast cancer does not actually have the disease, such an incorrect prediction is called FP.   



False Positive (FP) 

The false negative (FN), together with false positive (FP), is a type of inaccurately predicted outcome, meaning that the prediction does not match the true observation/ condition in terms of the class (positive/ negative).  
 
FN occurs when a negative class prediction (classification) was inaccurately made when a positive class prediction, when accurately predicted, should have been made.  


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