We have a dataset of patients that has a specific tumor type. Each data point has the size of the tumor as its single feature and an output that shows whether a tumor is really a cancer (y=1) or not (y=0). The logistic regression model for this data is: he(x) = g(x/4-7) Here x is the size of the tumor and g(z) is the sigmoid function, whereg (z) = /1 + e-z We have the following test set that has five patients. 2 3 4 5 6 7 8 42 36 72 14 0 0 18 22 25 1 1 1 0 Patient Size Cancer? Prediction Finally recall the error metric that is given in the lecture: m err (ho (x(0)), y(1) 1 12 0 0 Where err (he (x(i)), y(i)) = 0 if your prediction is correct and 1 otherwise. Use natural logarithm (LN) whenever necessary. a) (10) Find the confusion matrix of this algorithm. Please use the table below. Also use the last line of the above table to create the confusion matrix. Show your calculations. TN FP | FN TP