Q1: Linear model for regression above, what is X 2 3 4 Consider a linear-regression model with n = 1 and m = 3 with (X, Y) pairs as shown in the table JE (W) (the gradient of MSE with respect to w), when w = 0 and w = 1? W Q2: Linear model for classification (perceptron) X 1 0 Y 15 20 40 A. What is the value of wo, W, W if you perform the perceptron algorithm on the following training set when the initial weight of w={0,1,1), Iterations=2, activation function = sign, threshold =0, a=1 X 1 1 B. Draw the decision boundary using the estimated weight. Y 1 -1 Q3: Linear Model for classification (logistic regression) 1. Suppose you have trained a logistic regression classifier for cancer prediction (cancer is the positive class) given the cell size (x), and the best estimated parameters are wo= 0.2,w = 0.1. What is the probability that a patient cell is cancerous if the cell size (x) = 3.5 centimeters? Q1: Linear model for regression above, what is X 2 3 4 Consider a linear-regression model with n = 1 and m = 3 with (X, Y) pairs as shown in the table JE (W) (the gradient of MSE with respect to w), when w = 0 and w = 1? W Q2: Linear model for classification (perceptron) X 1 0 Y 15 20 40 A. What is the value of wo, W,W if you perform the perceptron algorithm on the following training set when the initial weight of w={0,1,1}, Iterations=2, activation function = sign, threshold =0, a=1 X 1 1 B. Draw the decision boundary using the estimated weight. Y 1 -1 Q3: Linear Model for classification (logistic regression) 1. Suppose you have trained a logistic regression classifier for cancer prediction (cancer is the positive class) given the cell size (x), and the best estimated parameters are wo= 0.2,w = 0.1. What is the probability that a patient cell is cancerous if the cell size (x) = 3.5 centimeters?