Question

A.Consider the following table showing results of a binary classification problem with validation data. Actual Class | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 Predicted Class | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 Build the confusion matrix. Compute Classifier accuracy, Precision, Recall, and F-score for "Class 1" based on the above data. [2+0.5+0.5+0.5+0.5 = 4 marks] B. Suppose you are building a classifier that helps in predicting whether a transaction is fraudulent. Explain precision and recall in this context (DON'T WRITE PRECISION AND RECALL DEFINITION). Which one do you think is more important and a better metric in this case? [1+1+2 = 4 Marks]

          A.Consider the following table showing results of a binary classification problem with validation data.

Actual Class | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1
Predicted Class | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0

Build the confusion matrix. Compute Classifier accuracy, Precision, Recall, and F-score for "Class 1" based on the above data.
[2+0.5+0.5+0.5+0.5 = 4 marks]

B. Suppose you are building a classifier that helps in predicting whether a transaction is fraudulent. Explain precision and recall in this context (DON'T WRITE PRECISION AND RECALL DEFINITION). Which one do you think is more important and a better metric in this case?
[1+1+2 = 4 Marks]

A.Consider the following table showing results of a binary classification problem with validation data.

Actual Class | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1
Predicted Class | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0

Build the confusion matrix. Compute Classifier accuracy, Precision, Recall, and F-score for "Class 1" based on the above data.
[2+0.5+0.5+0.5+0.5 = 4 marks]

B. Suppose you are building a classifier that helps in predicting whether a transaction is fraudulent. Explain precision and recall in this context (DON'T WRITE PRECISION AND RECALL DEFINITION). Which one do you think is more important and a better metric in this case?
[1+1+2 = 4 Marks]

Added by Andrea W.

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