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Multioutput Regression models in MATLAB

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I am working on a project where I need to predict multiple response variables for a given data set likely using random forests or boositng. Are there any functions I could use that might provide what I am looking for. Basically, what I mean is:
data = (2-D matrix of regressors)
regression model = regression_function(data,response variables)

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Ive J
Ive J il 6 Giu 2023
I'm not aware of such a function in MATLAB, but you can loop over your target/response variables, and each time fit a new model. Something like this:
models = cell(numel(responseVars), 1);
for k = 1:numel(models)
models{k} = fitrensemble(data(:, [features, responseVars(k)], responseVars(k)); % data table contains all features + outcomes
  7 Commenti
the cyclist
the cyclist il 8 Giu 2023
fitcecoc doesn't fit multiple response variables. It fits a single (categorical) response variable that has more than two categories.
Ive J
Ive J il 8 Giu 2023
Modificato: Ive J il 8 Giu 2023
Yes, that's correct and I didn't mean fitcecoc is multivariate. For multivariate SVM one could check sklearn. But for this specific problem of OP, I meant something like this by aggregating different responses to see how one label vs others could differ compared to separate SVMs:
y1 = ["y1-1", "y1-2", "y1-3"];
y2 = ["y2-1", "y2-2"];
y_multi = y1' + "_" + y2;
y_multi = categorical(y_multi(:))
y_multi = 6×1 categorical array
y1-1_y2-1 y1-2_y2-1 y1-3_y2-1 y1-1_y2-2 y1-2_y2-2 y1-3_y2-2

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the cyclist
the cyclist il 6 Giu 2023
The only MATLAB function (that I know of) that can handle multiple response variables is mvregress. Take a look at my answer here for examples with some common design matrices. There are of course examples in the documentation page I linked, as well.




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