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mhjensen
2020-09-16 18:50:58 +02:00
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doc/src/Projects/2020/Project1/Project1.do.txt
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@@ -230,19 +230,15 @@ Note also that when you calculate the bias, in all applications you don't know t
The aim here is to write your own code for another widely popular
resampling technique, the so-called cross-validation method. Again,
before you start with cross-validation approach, you should scale your
data and split it in test and training data as you did earlier.
Perform a resampling of the data where you split the data in training
data and test data using for example
data.
Implement the $k$-fold cross-validation algorithm (write your own
code) and evaluate again the MSE function resulting
from the test data. You can compare your own code with that from
from the test folds. You can compare your own code with that from
_Scikit-Learn_ if needed.
Compare the MSE you get from your cross-validation code with the one you got from your _bootstrap_ code.
You can also compare your own cross-validation code with the one provided by _Scikit-Learn_.
Compare the MSE you get from your cross-validation code with the one you got from your _bootstrap_ code. Comment your results. Try $5-10$ folds.
You can also compare your own cross-validation code with the one provided by _Scikit-Learn_.
=== Part d): Ridge Regression on the Franke function with resampling ===