$6\mathrm{.}16$ Some Machine Learning algorithms are trained on data sets that have known outcomes. If Yobs is a vector of observed data values, and Ypred is a vector of predicted data values, the error vector is Yobs $-$ Ypred, and the Mean Squared Error is defined as the mean of the squares of the errors, or:

MSE $=$ mean $(($ Yobs $-$ Ypred ${)}^{{?}^{{?}^{?}}}2)$

A script:

calls a function to get Yobs and Ypred

calls a function to calculate and return the error vector and the MSE

calls a function to print the MSE

The script and a stub for the first function are given here. Write the other two functions, in separate code files.

HW$4$ Pr $616.m$

clear, clc

$\%$ comments are required

$[$yobs$,$ ypred$]=$ getvecs;

$[$errorvec$,$ mse$]=$ findmse$($yobs$,$ ypred$)$;

printmse $($mse$)$

getvecs.m

function $[$yobs$,$ ypred$]=$ getvecs

$\%$ comments are required

yobs $=3$:$7$;

ypred $=\left[\begin{array}{ccccc}2\mathrm{.}8& 4& 5\mathrm{.}1& 5\mathrm{.}9& 7\mathrm{.}3\end{array}\right]$;

end. Please help! I don't know how to write this in MatLab!