Part $4$: Automating the Parameter Search

On the previous problem we were able to choose some OK parameters just by hand$-$tuning. But in real life $($where time is money$)$ it would be better to do something a little more automated. One common thing to do is a grid$-$search over a predefined range of the parameters. In this case you will loop over all possible combinations of parameters, estimate the accuracy of your model using K$-$Folds cross$-$validation, and then choose the parameter combination that produces the highest validation accuracy.

Part A: Below is an experiment where we search over a logarithmic range between $25$

and $25$

for $$

and a range between $25$

and $25$

for $$

$.$ For the accuracy measure we use K$-$Folds CV with $=3$

$.$