Q1: Linear model for regression above, what is X 2 3 4 Consider a linear-regression model...

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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 w₁), when w₁ = 0 and w₁ = 1? W₁ Q2: Linear model for classification (perceptron) X₁ 1 0 Y 15 20 40 A. What is the value of wo, W₁, W₂ 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 X₂ 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 (x₁), and the best estimated parameters are wo= 0.2,w₁ = 0.1. What is the probability that a patient cell is cancerous if the cell size (x₁) = 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 w₁), when w₁ = 0 and w₁ = 1? W₁ Q2: Linear model for classification (perceptron) X₁ 1 0 Y 15 20 40 A. What is the value of wo, W₁,W₂ 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 X₂ 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 (x₁), and the best estimated parameters are wo= 0.2,w₁ = 0.1. What is the probability that a patient cell is cancerous if the cell size (x₁) = 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 w₁), when w₁ = 0 and w₁ = 1? W₁ Q2: Linear model for classification (perceptron) X₁ 1 0 Y 15 20 40 A. What is the value of wo, W₁, W₂ 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 X₂ 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 (x₁), and the best estimated parameters are wo= 0.2,w₁ = 0.1. What is the probability that a patient cell is cancerous if the cell size (x₁) = 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 w₁), when w₁ = 0 and w₁ = 1? W₁ Q2: Linear model for classification (perceptron) X₁ 1 0 Y 15 20 40 A. What is the value of wo, W₁,W₂ 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 X₂ 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 (x₁), and the best estimated parameters are wo= 0.2,w₁ = 0.1. What is the probability that a patient cell is cancerous if the cell size (x₁) = 3.5 centimeters?

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Lets address each question one by one Q1 Linear model for regression We are given a linear regression model with parameters w0 and w1 The gradient of Mean Squared Error MSE with respect to w1 can be c... View the full answer