MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The most frequently used performance metrics for classification according to these values are accuracy (ACC), precision (P), sensitivity (Sn), specificity (Sp), and F-score values. The calculation of these performance metrics according to the values in the confusion matrix is made according to Eqs. (14.49)–(14.53).

Detailed explanation-2: -In your case understand that the 4*4 matrix denotes that you have 4 different values in your predicted variable, namely:AGN, BeXRB, HMXB, SNR. One thing more, the correct classification of the values will be on the diagonal running from top-left to bottom-right and all the other values are misclassified.

Detailed explanation-3: -Confusion Matrix gives a comparison between Actual and predicted values. The confusion matrix is a N x N matrix, where N is the number of classes or outputs. For 2 class, we get 2 x 2 confusion matrix. For 3 class, we get 3 X 3 confusion matrix.

Detailed explanation-4: -Thus, the sum of each row in a balanced and normalized confusion matrix is 1.00, because each row sum represents 100% of the elements in a particular topic, cluster, or class.