Wsrtjtyk

I have a Matlab table that contains information on students (numerical and categorical). A sample is given here:

School = {'GB'; 'UR'; 'GB'; 'GB'; 'UR'};

School = categorical(School);

Age = [14;14;12;16;19];

Relationship = {'yes'; 'yes'; 'no'; 'no'; 'yes'};

Relationship = categorical(Relationship);

Status = {'ft'; 'pt'; 'ft'; 'ft'; 'ft'};

Status = categorical(Status);

Father_Job = {'pol'; 'ser'; 'oth'; 'ele'; 'cle'};

Father_Job = categorical(Father_Job);

Health = [1;2;3;3;5];

Exam = {'pass'; 'pass'; 'fail'; 'fail'; 'pass'};

Exam = categorical(Exam);



T =

School    Age    Relationship    Status    Father_Job    Health    Exam

______    ___    ____________    ______    __________    ______    ____



  GB      14         yes           ft         pol          1       pass

  UR      14         yes           pt         ser          2       pass

  GB      12         no            ft         oth          3       fail

  GB      16         no            ft         ele          3       fail

  UR      19         yes           ft         cle          5       pass

I want to use this data to predict and classify the pass/fail of the exam. I am planning to use the fitglm to make a logistic regression, and fitcnb to make a Naive Bayes classifier. I know that both methods can handle well categorical variables in Matlab, so there should be no problem using my table as it is, with its categorical variables.

However, I have a problem when I want to use cvpartition and crossvalind to perform a 10-fold cross-validation. When I try to create indices of my folds, I get the following error: Error using statslib.internal.grp2idx Subscripting a table using linear indexing (one subscript) or multidimensional indexing (three or more subscripts) is not supported. Use a row subscript and a variable subscript.

My goal is to perform the following operations:

% Column 7 (Exam) is the response variable

X = T(:, 1:6);

Y = T(:, 7);



% Create indices of 5-fold cross-validation (here I get errors)

cvpart = cvpartition(Y,'KFold',5);

indices = crossvalind('Kfold',Y,5);



% Create my test and training sets

for i = 1:5

 test = (indices == i); 

 train = ~test;

 Xtrain = X(train,:);

 Xtest = X(test,:);

 Ytrain = Y(train,:);

 Ytest = Y(test,:);

end



% Fit logistic model

mdl = fitglm(Xtrain,Ytrain,'Distribution','binomial')

Would anyone have a take on this please? I know that I can possibly change the categorical variables to numerical ones, but I would rather not. Is there anyway around this? Thank you.

I think your main problem is that your data set is just too small. You have n =5, which isn't even enough to create a non-validated model.