Suppose 10,000 samples including 9,900 for benign and 100 for malignant in cancer diagnostics. Using a newly developed method, the researcher observed that 10 (out of 9,900) benign samples were misclassified into malignant and 3 (out of 100) malignant samples were misclassified into benign, while the rest of samples were classified correctly.

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a. Construct the confusion matrix. Actually Positive Actually Negative (malignant) (benign ) Predicted Positive ( malignant) Predicted Negative (benign) b. Determine TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative) from the constructed confusion matrix. C. (Calculate accuracy, false positive rate, and false negative rate. Accuracy = (TP+TN)/ (TP+TN+FP+FN) = False positive rate = FP / (TN+FP) = False negative rate = FN / (TP+FN) =