{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

HW5 - (Computer Exercise Faculty of Computers and...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
Faculty of Computers and Information Faculty of Computers and Information (Computer Exercise) Department of Computer Science Pattern Recognition A First Simulation Example on Designing and Assessing a Regression Func- tion Generate one small (10 observations) training dataset from binormal distribution (representing a response and a pre- dictor) with mean vector (0 , 0) , unit variance, and ρ = 0 . 8 . Fit the data to a linear model, calculate the apparent MSE given by 1 N RSS . Plot the linear model, the data, and the best regression function (which is the conditional expectation of a bivariate normal) on the same graph. Generate a large data set (1000 observations to represent the population) from the same distribution and calculate the true error rate (the MSE or the risk) of your model on this data set; we denote this by err tr . This is the performance conditional on the training set above. Obtain the performance of best regression function as well; denote it by err .
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

Ask a homework question - tutors are online