How to cross-validate a model created by TreeBagger?

I created a Random Forest model using TreeBagger. Now I want to cross-validate the model but it does not work using crossval. I think crossval requires another model type than the type that TreeBagger creates. Does anybody know how to implement the cross-validation for my model that I created with TreeBagger?

rfmodel = TreeBagger(ntrees, X, Y, 'Method', 'regression')
cvrfmodel = crossval(rfmodel,'kfold',10);

[Marta Caneda Portela]

Hi! did you ever get a solution? I am trying to do the same and cannot find anything online :)

Answer 1

[Ayush Aniket]

The reason your code does not work is because of the syntax for crossval function in the code line:

cvrfmodel = crossval(rfmodel,'kfold',10);

One of the syntax that the crossval function supports is the following:

values = crossval(fun,X);

It performs 10-fold cross-validation for the function fun, applied to the data in X.

Refer to the following documentation to read about the process of defining a function incorporating any model (like the Treebagger object): https://www.mathworks.com/help/stats/crossval.html#mw_240ecf56-c164-4009-aba7-033f9c3b25cb

Another approach is to manually split your data and perform cross-validation using the cvpartition function. Below is an example code demonstrating this method with Mean Squared Error as the loss function:

cv = cvpartition(size(X, 1), 'KFold', 10);

% Initialize an array to store the mean squared error for each fold
mseValues = zeros(k, 1);

% Perform k-fold cross-validation
for i = 1:k
    % Get training and validation indices
    trainIdx = training(cv, i);
    testIdx = test(cv, i);
    
    % Train the Random Forest model on the training set
    rfmodel = TreeBagger(ntrees, X(trainIdx, :), Y(trainIdx), 'Method', 'regression');
    
    % Predict on the validation set
    y_pred = predict(rfmodel, X(testIdx, :));
    
    % Calculate the mean squared error for this fold
    mseValues(i) = mean((Y(testIdx) - y_pred).^2);
end

% Calculate the average MSE across all folds
averageMSE = mean(mseValues);

You can read about the cvpartition function at the following link: https://www.mathworks.com/help/stats/cvpartition.html