# Calculate Accuracy, Precision, Recall and F1 Score for the following Confusion Matrix on Heart Attack Risk.

Calculate Accuracy, Precision, Recall and F1 Score for the following Confusion Matrix on Heart Attack Risk. Also suggest which metric would not be a good evaluation parameter here and why?

Select the correct answer from above options

by (1.7m points)

The Confusion Matrix Reality: 1 Reality: 0

Prediction: 1 50 20

Prediction: 0 10 20

The Confusion Matrix Reality: 1 Reality: 0

Prediction: 1 50 20 70

Prediction: 0 10 20 30

60 40 100

Calculation:

Accuracy: Accuracy is defined as the percentage of correct predictions out of all the observations.

Where True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN). Accuracy = (50+20) / (50+20+20+10)

= (70/100)

= 0.7

Precision: Precision is defined as the percentage of true positive cases versus all the cases where the prediction is true.

= (50 / (50 + 20))

= (50/70)

= 0.714

Recall: It is defined as the fraction of positive cases that are correctly identified.

= 50 / (50 + 60)

= 50 / 110

= 0.5

F1 Score: F1 score is defined as the measure of balance between precision and recall.

= 2 * (0.714 *0.5) / (0.714 + 0.5)

= 2 * (0.357 / 1.214)

= 2* (0.29406)

= 0.58

Therefore,

Accuracy= 0.7 Precision=0.714 Recall=0.5

F1 Score=0.588

Here within the test there is a tradeoff. But Recall is not a good Evaluation metric. Recall metric needs to improve more.

Because,

False Positive (impacts Precision): A person is predicted as high risk but does not have heart attack.

False Negative (impacts Recall): A person is predicted as low risk but has heart attack. Therefore, False Negatives miss actual heart patients, hence recall metric need more improvement.

False Negatives are more dangerous than False Positives.