The performance of a classifier is as given in the confusion matrix below. The training dataset...

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The performance of a classifier is as given in the confusion matrix below. The training dataset consisted of 1000 patient data based on some medical research to identify if the patient has the rare disease. [8 marks] Actuals Positive Negative Model Prediction Positive Negative 500 20 190 290 (a) Calculate the following statistics for the above confusion matrix: 1. Accuracy of the classification 2. Overall how often it was wrong? 3. When it predicts yes, how often is it correct? 4. When it's actually no, how often does it predict yes? 5. When it's actually no, how often does it predict no? 6. When it's actually yes, how often does it predict yes? (b) Looking at the accuracy, sensitivity and specificity what can you say about the Activate classifier's performance? Go to Sett The performance of a classifier is as given in the confusion matrix below. The training dataset consisted of 1000 patient data based on some medical research to identify if the patient has the rare disease. [8 marks] Actuals Positive Negative Model Prediction Positive Negative 500 20 190 290 (a) Calculate the following statistics for the above confusion matrix: 1. Accuracy of the classification 2. Overall how often it was wrong? 3. When it predicts yes, how often is it correct? 4. When it's actually no, how often does it predict yes? 5. When it's actually no, how often does it predict no? 6. When it's actually yes, how often does it predict yes? (b) Looking at the accuracy, sensitivity and specificity what can you say about the Activate classifier's performance? Go to Sett

Answer rating: 100% (QA)

The Confusion Matrix given in the question contains information about 1000 Patients Before moving further first sort out all the information shown in the given confusion matrix data True Positive TP 5... View the full answer