Kizspy | Question: 5
(Choose 1 answer)
You're constructing a polynomial regression model to estimate the temperature of a city based on the time of
day (in hours). The model's equation is T=a+b+x+c x^2, where T is the temperature in degrees Celsius, x is the
time of day in hours, and a, b, and c are coefficients. You're using the Mean Squared Error (MSE) loss
function. The training data (x,T) pairs are [(1, 4), (2,9), (3, 16), (4, 25)]. The initial values for a, b, and c are 0.8,
1.7, and 0.9, respectively. Employing gradient descent with a learning rate set to 0.01, what are the values of a,
b, and c after the first optimization iteration? Note that we feed first iteration with the first 3 data pairs
(batch_size=3)!
A. a 0.82, b 1.74, c=1.02
B. a=0.83, b=1.77, c=1.07
C. a=0.83, b=1.77, c=1.09
D. a=0.84, b=1.78, c=1.10
