Confusion Matrix -- Is it a confusion all the time ?

Part -2 of confusion matrix

How to read a confusion matrix

Supposing the machine predicts that a person gets a particular disease.

 the matrix displays as

	Confirmed	Not confirmed
Confirmed	35	20
Not confirmed	25	15

What do you understand? This has to be understood as the rows denote the actual values and the columns form the predicted values. Let us go deep now.

 I have given summation of the rows and columns

		Predicted values
		Confirmed	Not confirmed	total
Actual values	Confirmed	35	20	55
	Not confirmed	25	15	40
total		60	35	95

Here in the above matrix , the important class is 'confirmed.' We don't bother about the other part. 

Now comes the terminologies. Specificity

Again what do we mean? The machine predicts that 35 people has a disease against the total count of 55.

Secondly the machine predicts that 15 people do not have a disease against the total of 40 people without a disease.

What should we take from this ?

35/55  =.6363 or  the specificity is 63 .63 %  We expect the machine to be accurate about 90-95 or still higher as the case may be to predict people with a disease.

 Sensitivity= True positive / (true positive + False negative ) -----> The sensitive part of our problem

Secondly the machine predicts 15/40 don't have a disease. We are not too much bothered about it. 

ie 37.5 %  is the specificity.

Specificity= true negative / ( false positive + true negative)