I've been trying to create a multivariate regression model to fit my training data into the prediction of a value. I've put my data into a matrix
m x n where
m is the number of instances and
n the number of features/predictors. My label vector is then
m x 1. This is my code to predict the theta values, or parameters.
theta_matrix = pinv(X'*X)*X'*y_label;
Now I want to slip the data into train and test, and by researching I've found that
cross-validation in 10-fold can be a good option. If I do so, wouldn't it get me 10 sets of parameters theta? So what to choose from then?
And about feature selection, I've found that
stepwise can be a good choice, but I think it does not take into account that features can be correlated. Any alternative?