Kizspy Question: 49
(Choose 1 answer)
You're developing a logistic classifier model to predict whether the temperature in a city is high or low based
on the time of day (in hours). The model's equation is prob(T) = sigmoid(a + b*x), where prob(T) represents
the probability of high temperature (1) or low temperature (0), x is the time of day in hours, and a and b are
coefficients. You're using the Binary Cross Entropy loss function and gradient descent for optimization. The
initial values for a and b are 1.2 and 0.8, respectively. Given the training data (x, T) pairs: [(3, 0), (5,0), (13, 1),
(14, 1)], and a learning rate of 0.03, what are the values of a and b after the first optimization iteration, where
the first iteration uses all data pairs (batch_size=4)?
A. a 1.14, b=0.79
B. a 1.08, b=0.77
C. a 1.02, b=0.76
D. a 0.96, b=0.74
