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a) What will be the accuracy of the model? (TP/(TP+FP+FN+TN)) b) Recall of the model(TP/(TP+FN)) c) Precision of the model(TP/(TP+FP)) Accuracy=.965, Recall =0.39, Precision=0.30 Accuracy=.965, Recall=0.30, Precision=0.39 Accuracy=.99, Recall-0.21, Precision=0.14 Accuracy=.99, Recall-0.31, Precision=0.34

a) What will be the accuracy of the model? (TP/(TP+FP+FN+TN)) b) Recall of the model(TP/(TP+FN)) c) Precision of the model(TP/(TP+FP)) Accuracy=.965, Recall =0.39, Precision=0.30 Accuracy=.965, Recall=0.30, Precision=0.39 Accuracy=.99, Recall-0.21, Precision=0.14 Accuracy=.99, Recall-0.31, Precision=0.34

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A No 140 9560 a) What will be the accuracy of the model? (TP/(TP+FP+FN+TN)) b) Recall of the model(TP/(TP+FN)) c) Precision of the model(TP/(TP+FP)) Accuracy=.965, Recall =0.39, Precision=0.30 Accuracy=.965, Recall-0.30, Precision=0.39 Accuracy=.99, Recall-0.21, Precision=0.14 Accuracy=.99, Recall-0.31, Precision=0.34
Transcribed Image Text:A No 140 9560 a) What will be the accuracy of the model? (TP/(TP+FP+FN+TN)) b) Recall of the model(TP/(TP+FN)) c) Precision of the model(TP/(TP+FP)) Accuracy=.965, Recall =0.39, Precision=0.30 Accuracy=.965, Recall-0.30, Precision=0.39 Accuracy=.99, Recall-0.21, Precision=0.14 Accuracy=.99, Recall-0.31, Precision=0.34
The table below shows a confusion matrix for medical data where the class values are yes and no for a class label attribute, cancer. Actual Cancer Yes No Predicted Yes 90 140 No 210 9560 a) What will be the accuracy of the model? (TP/(TP+FP+FN+TN)) b) Recall of the model(TP/(TP+FN)) c) Precision of the model(TP/(TP+FP)) Accuracy=.965, Recall =0.39, Precision=0.30
Transcribed Image Text:The table below shows a confusion matrix for medical data where the class values are yes and no for a class label attribute, cancer. Actual Cancer Yes No Predicted Yes 90 140 No 210 9560 a) What will be the accuracy of the model? (TP/(TP+FP+FN+TN)) b) Recall of the model(TP/(TP+FN)) c) Precision of the model(TP/(TP+FP)) Accuracy=.965, Recall =0.39, Precision=0.30
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