For a two-class classification problem, an artificially intelligent system was developed to predict a specie with labels either Type-0 or Other. 80% of the dataset were used for training purposes, while the remaining 20% of the dataset were used to test the system. The Confusion Matrix below shows the results. Assume Type-0 is Negative or 0 and Other is Positive or 1.

Predicted Labels
Type-0	Other
Actual Labels	Type-0	100	30
Other	10	37

For the Confusion Matrix given above, answer the following questions:

a. How many instances of Type-0 are misclassified?

(1)

b. How many instances of Other are correctly classified?

(1)

c. What is the value of False Positive?

(1)

d. What is the value of False Negative?

(1)

e. Is there any difference between Label Type-0 and Type-I Error? Justify your answer.

(2)

f. What is Accuracy, Precision, and Recall? Give percentage values for each measure.

(3)

g. What is percentage Error?

(1