Day 49 in MIT Sloan Fellows Class 2023, Advanced Data Analytics and Machine Learning in Finance 2, Confusion matrix

The confusion matrix is super confusing.

https://www.researchgate.net/figure/Confusion-matrix-and-performance-equations-The-confusion-matrix-included-four_fig1_340034692

Everytime I forget the definition of TP, FP, etc, and metrics as well. 

• True positives (TP) are positive outcomes that the model predicted correctly.
• True Negatives (TN) are negative outcomes that the model predicted correctly.
• False Negatives (FN) are negative outcomes that the model predicted incorrectly. 
• False Positives (FP) are positive outcomes that the model predicted incorrectly.

So if it is a cancer test, TP means we predict cancer correctly.

TN means we did not predict cancer and patient actually does not have cancer.

FP is we predict a patient has cancer, but actually he or she does not have. 

FN is we predict a patient is healthy, but actually he or she has a cancer.

• Sensitivity: How many positive people you can predict from positive cohort?
• Accuracy: How many you correctly predicted in all the cohort?