Kizspy | Question: 11
(Choose 1 answer)
You're building a polynomial regression model to predict crop yield based on rainfall. The model's equation is
y=a+b x+c x^2, where a, b, and c are coefficients, and x represents rainfall in millimeters. You're using the
Mean Squared Error (MSE) loss function. The training data (x,y) 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 first data pair (batch_size=1)!
A. a 0.80, b 1.70, c=0.90
B. a=0.81, b=1.71, c=0.93
C. a=0.82, b=1.74, c=1.00
D. a=0.82, b=1.76, c=1.10
