Answered step by step
Verified Expert Solution
Question
1 Approved Answer
1. Failure of k-fold cross validation: Consider a case in which the label is chosen at random according to P[y=1]=P[y=0]=1/2. Consider a learning algorithm that
1. Failure of k-fold cross validation: Consider a case in which the label is chosen at random according to P[y=1]=P[y=0]=1/2. Consider a learning algorithm that outputs the constant predictor h(x)=1 if the parity of the labels on the training set is 1 and otherwise the algorithm outputs the constant predictor h(x)=0. Prove that the difference between the leave-one-out estimate and the true error in such a case is always 1/2. 2. Occam's razor: Let D be dataset, H be models and be model parameters. ^=argmaxp(D,H) Assuming a broad Gaussian prior distribution and iid observations, derive an approximation for the log evidence logp(DH), in terms of the dataset size N, 's dimensionality m, and logp(D)
Step by Step Solution
There are 3 Steps involved in it
Step: 1
Get Instant Access to Expert-Tailored Solutions
See step-by-step solutions with expert insights and AI powered tools for academic success
Step: 2
Step: 3
Ace Your Homework with AI
Get the answers you need in no time with our AI-driven, step-by-step assistance
Get Started