With the help of a confusion matrix, we can compute-RecallPrecissionAccuracyAll of the above

All of the above. With the help of a confusion matrix, we can compute Recall, Precision, and Accuracy.

Here's how:

1. Recall: Also known as sensitivity, hit rate, or true positive rate (TPR), it measures the proportion of actual positives that are correctly identified. It is defined as the ratio of true positives (TP) to the sum of true positives and false negatives (FN). It is computed as Recall = TP / (TP + FN).

2. Precision: Also known as positive predictive value, it measures the proportion of actual positives that are correctly identified. It is defined as the ratio of true positives (TP) to the sum of true positives and false positives (FP). It is computed as Precision = TP / (TP + FP).

3. Accuracy: It measures the proportion of true results (both true positives and true negatives) in the population. It is computed as Accuracy = (TP + TN) / (TP + TN + FP + FN).

So, a confusion matrix is a handy tool to compute all these metrics.