A confusion matrix (or error matrix) is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it’s called the matching matrix.
The confusion matrix has two parameters:
Actual
Predicted
It also has identical sets of features in both of these dimensions.
Consider a confusion matrix (binary matrix) shown below:
Confusion Matrix
Here,
For actual values:
Total Yes = 12+1 = 13
Total No = 3+9 = 12
Similarly, for predicted values:
Total Yes = 12+3 = 15
Total No = 1+9 = 10
For a model to be accurate, the values across the diagonals should be high. The total sum of all the values in the matrix equals the total observations in the test data set.
For the above matrix, total observations = 12+3+1+9 = 25
Now, accuracy = sum of the values across the diagonal/total dataset
= (12+9) / 25
= 21 / 25
= 84%
