update on files

doc/Projects/2020/Project1/html/Project1.html

@@ -122,7 +122,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Sep 7, 2020</h4></center> <!-- date -->
+<center><h4>Sep 16, 2020</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Regression analysis and resampling methods  </h2>
@@ -359,18 +359,16 @@ 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.
 
 <p>
 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
 <b>Scikit-Learn</b> if needed.
 
 <p>
-Compare the MSE you get from your cross-validation code with the one you got from your <b>bootstrap</b> code.
+Compare the MSE you get from your cross-validation code with the one you got from your <b>bootstrap</b> code. Comment your results. Try \( 5-10 \) folds. 
 You can also compare your own cross-validation code with the one provided by <b>Scikit-Learn</b>.
 
 <h3 id="___sec4">Part d): Ridge Regression on the Franke function  with resampling </h3>