Question 2 (a) Use Python to compute the weight vector w w 0 , w 1 , w 2 T for linear regression of N input data points ( x i , y i ) that minimizes the sum of squared error ( w T x i y i ) 2 ( use the method in slide 29 in lecture 2 ) The input data are x 1 (1,3,2) T y 1 3 x 2 (1,2,2) T y 2 1 x 3 (1,1, 1) T y 3 1 x 4 (1, 1, 3) T y 4 3 The first element in vector x i is always x 0 1 (See slides in lecture 1) Intermediate steps of the computation are needed (20 points) (b) Use the linearRegression module in Python package sklearn to find w for linear regression Steps of the computation are needed (5 points)

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Question: Question 2. (a) Use Python to compute the weight vector w = [ w 0 , w 1 , w 2 ] T for linear

Question 2. (a) Use Python to compute the weight vector w = [w₀,w₁,w₂]^T for linear regression of N input data points (x_i,y_i) that minimizes the sum of squared error (w^Tx_i y_i)² (use the method in slide 29 in lecture 2). The input data are

x₁=(1,3,2)^T y₁ = 3

x₂=(1,2,2)^T y₂ = 1

x₃=(1,1,-1)^T y₃ = -1

x₄=(1,-1,-3)^T y₄= -3

The first element in vector x_i is always x₀=1 (See slides in lecture 1). Intermediate steps of the computation are needed (20 points). (b) Use the linearRegression module in Python package sklearn to find w for linear regression. Steps of the computation are needed (5 points).

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