Re: BiasVariance Analysis
Dear Prof. Mostafa,
The two hypothesis sets are:
g1(x) = b
g2(x) = α4 . x^4+ α3 x^3 + α2 x^2 + α1 +b
Analyze the decomposition of the generalization error into bias + variance + noise
by generating random samples of size N = 10, fitting the models gi , and determining the predictions and prediction errors for x = 0, 1/100, . . . , 10.
How to generalize noise and during the calculation of bias and variance, how can we ignore the error e in the target function?
How to determine the predictions and prediction errors for different values of x?
