crossval finds the k-fold cross validation loss of a given model with respect to a given dataset. I don’t see how you can use fitrtree along with that to find the optimal ridge parameter ‘k’ for ridge. It is a model that you’re seeking to find in the first place.
Have a look at the methods mentioned on this page: https://www.mathworks.com/help/stats/bayesian-optimization-workflow.html. They describe the many options available to optimize the hyperparameter for a wide variety of problems, including ridge regression.
Here’s an example at https://www.mathworks.com/help/stats/fitrlinear.html#bu5tqz1-1 if you want to test the performance of your model over a range of possible choices for the ridge parameter (‘Lambda’ Regularization Term Strength).
On the other hand, you can have the function optimize for the ridge parameter. Have a look at the example at https://www.mathworks.com/help/stats/fitrlinear.html#bvevent-1. This might be best suited for you. Set the name-value pair ‘OptimizeHyperparameters’ to {‘Lambda’}.
