Here is my code and my questions are at the end of the code.
%% Predict Values Using Ridge Regression
% Predict miles per gallon (MPG) values using ridge regression.
%
% Load the |carbig| data set.
clearvars;
load carbig
X = [Acceleration Weight Displacement Horsepower];
y = MPG;
% remove NaN entries in y
idxNaN=find(isnan(y));
X(idxNaN,:)=[];
y(idxNaN)=[];
% remove NaN entries in remaining X
[r, c] = find(isnan(X));
idxNaN=unique(c);
X(idxNaN,:)=[];
y(idxNaN)=[];
%%
% Split the data into training and test sets.
n = length(y);
rng('default') % For reproducibility
c = cvpartition(n,'HoldOut',0.3);
idxTrain = training(c,1);
idxTest = ~idxTrain;
%%
%Predict miles per gallon (MPG) values using ridge regression
mdl2=fitrlinear(X(idxTrain,:),y(idxTrain),'Learner','leastsquares','Regularization','ridge','CrossVal','on','KFold',10)
% examine one CV model
mdl2.Trained{2}
% If I change the number in {}, I get slightly different Lambda. How is it
% determined? If it's determined by the cross-validation, then test error
% can't be derived from yhat.
% I assume that I cannot use predict function on the test data based on my
% understanding.
% yhat2=predict(mdl2,X(idxTest,:));
% what is the best way to train a model using the entire training data
% (i.e., idxTrain) based on the cross-validation models?
