We want to fit a logistic regression model to some data. The model is of the...

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We want to fit a logistic regression model to some data. The model is of the form y = ho(x) = o(0o + 01x1 + 02x2), and the error function for m datapoints is given by E(0) = -log[II, (ho(x'))"" (1– ho(x*))!-']. %3D Consider the first training datapoint: (x1,x2) = (2,3) from class y = 0. Perform one iteration of gradient descent with this data point. The initial parameters are (80,01, 02) = (-1, –1,0.5) and a = 0.1. Which answer most closely resembles the updated values of the parameters? Select one: a. (60,01, 02) = (-1,05, –1.09, 0.41) b. (60,01, 02) = (-1.88, -2.06, 0.76) c. (80, 01,02) = (-1.02, –1.04, 0.45) We want to fit a logistic regression model to some data. The model is of the form y = ho(x) = o(0o + 01x1 + 02x2), and the error function for m datapoints is given by E(0) = -log[II, (ho(x'))"" (1– ho(x*))!-']. %3D Consider the first training datapoint: (x1,x2) = (2,3) from class y = 0. Perform one iteration of gradient descent with this data point. The initial parameters are (80,01, 02) = (-1, –1,0.5) and a = 0.1. Which answer most closely resembles the updated values of the parameters? Select one: a. (60,01, 02) = (-1,05, –1.09, 0.41) b. (60,01, 02) = (-1.88, -2.06, 0.76) c. (80, 01,02) = (-1.02, –1.04, 0.45)

Answer rating: 100% (QA)

Answer Option B is correct Logistic regression is a statistical model that in its basic form uses a ... View the full answer