Machine Learning for Panel Data

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Nick
Nick 2022-4-26
编辑： Nick 2022-5-4
Can anyone please confirm whether Matlab supports machine learning methods for panel/longitudinal data?
I have not been able to find anything around trees/random forests etc.
Many thanks

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Hiro Yoshino
Hiro Yoshino 2022-4-27
Yes, it does.
table type variable is supported by many ML models. For example, SVM (https://jp.mathworks.com/help/stats/fitcecoc.html) accepts "Tbl" (table) as a first argument.
This is where "table" is elaborated https://jp.mathworks.com/help/matlab/tables.html
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Hiro Yoshino
Hiro Yoshino 2022-4-29
Looks like you need to get yourself familiarized with the MATLAB documentation a bit more.
Let's us take https://www.mathworks.com/help/stats/fitcecoc.html#namevaluepairarguments as an example.
You will see the available syntaxes as follows:
Mdl = fitcecoc(Tbl,ResponseVarName)
Mdl = fitcecoc(Tbl,formula)
Mdl = fitcecoc(Tbl,Y)
Mdl = fitcecoc(X,Y)
Mdl = fitcecoc(___,Name,Value)
[Mdl,HyperparameterOptimizationResults] = fitcecoc(___,Name,Value)
then you may want to puress the link to the "Name" & "Value" option. This option allows you to tune the model finely as you wish. In the list of this option, you'll find "PredictorNames" and "ResponseName". These are where you specify the predictors and the response respectively.
Nick
Nick 2022-5-4
编辑：Nick 2022-5-4
Thank you Hiro. Apologies for being repetitive but having gone through the links I still have trouble understanding this.
In this simple example below how exactly do I add the ID variable (data.ID) in this model given this is a panel data set where there are different borrowers (i.e. different IDs) with several observations under each ID?
load RetailCreditPanelData.mat
data = data(1:1000,:);
X = data(:,[2:3,5]);
Y = data.Default;
Model1 = fitcecoc(X,Y);

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