đź‘‰Â Note: Reading: HandsOn ML  Chap 3: Classification (section â€śConfusion Matricesâ€ť)
Confusion matrix

actual (yes) 
actual (no) 
predict (yes) 
TP 
FP 
predict (no) 
FN 
TN 
 True Positive (TP): what we predict Positive is really Positive.
 True Negative (FN): what we predict Negative is really Negative.
 False Negative (FN): what we predict Negative is actually Positive.
 False Positive (FP): what we predict Positive is actually Negative.
This guy is pregnant?
How to remember?
 True/False indicates what we predicted is right/wrong.
 Positive/Negative is what we predicted (yes or no).
Type I / Type II errors
 FP = Type I error = rejection of true null hypothesis = negative results are predicted wrongly = what we predict positive is actually negative.
 FN = Type II error = nonrejection of a false null hypothesis = positive results are predicted wrongly = what we predict negative are actually positive.
Why CM is important?
Give a general view about our model, "is it really good?" thanks to precision and recall!
Precision & Recall

actual (yes) 
actual (no) 

predict (yes) 
TP 
FP 
Precision 
predict (no) 
FN 
TN 


Recall 



Precision: How many of our positive predictions are really true? (Check the accuracy of our positive predictions).
$$
\mathrm {precision}= \dfrac{\mathrm{true\, positive}}{\mathrm{positively\, predicted\, results}}= \dfrac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}.
$$

Recall: How many of positive results belong to our predictions? (Do we miss some negative predictions?)
$$
\mathrm {recall}= \dfrac{\mathrm{true\, positive}}{\mathrm{positively\, actual\, results}}= \dfrac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}.
$$
Recognizing number 5. Figure taken from this book.
When to use?
 Precision is importantly used when the "wrongly predicted yes" (FP) influences much (e.g. This email is spam? â€” results yes but actually no and we lost important emails!).
 Recall (Sensitivity) is importantly used when the "wrongly predicted no" (FN) influences much (e.g. In the banking industry, this transaction is fraudulent? â€” results no but actually yes and we lost money!).