Question

A machine learning model is trained to predict tumor in patients. The test dataset consists of 100 people. Calculate the Accuracy, Precision, Recall and F1-Scorebased on the given confusion matrix. What is the weakness and strength of this model?

Answer 1

Step-by-step explanation:

Confusion matrix:

Actual Negative Actual Positive

Predicted Negative 50 20

Predicted Positive 10 20

Accuracy = (50 + 20) / 100 = 0.7

Precision = 20 / (20 + 10) = 0.67

Recall = 20 / (20 + 20) = 0.5

F1-Score = 2 * (Precision * Recall) / (Precision + Recall) = 0.57

A model's strength is measured by its ability to accurately predict the positive class, which is measured by precision, and by its ability to find all the positive instances, which is measured by recall. A model's weakness is measured by its ability to accurately predict the negative class, which is measured by the true negative rate.

In this case, the model has a high accuracy of 0.7, however, it has a low precision of 0.67 and recall of 0.5, and a low F1-Score of 0.57. This means that the model is not very good at correctly identifying positive cases, and is also not very good at correctly identifying negative cases. This model is not a good model for this problem and it is needed to improve the model.