Confusion Matrix

The following table summarize performance of a classification algorithm for identifying cancerous (Malignant) cells.

Table-1: Outcome from a classifier

Model M1		Prediction
		Benign	Malignant
Actual	Benign	60	15
	Malignant	5	35

1. Compute the accuracy and error rate of the classifier.
2. Compute the sensitivity and specificity of the classifier.
3. Compute the precision and recall of the classifier.
4. Compute the F₁ measure of the classifier.
5. Compute the F measure of the classifier in which weights recall twice as much as precision.