Check for missing argument or incorrect argument data type in call to function 'predict'.
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Tejal Mehta
on 6 Dec 2020
Commented: Tejal Mehta
on 6 Dec 2020
I am trying to predict values of cross-validated model using training data.But I am getting this error -
Check for missing argument or incorrect argument data type in call to function 'predict'.
The code I am using is as below -
mdlCv= fitctree(bank_Train,'y','KFold',10); %bank_Train is the training data
[label,scores] = predict(mdlCv,bank_Test); %bank_Test is the testing data
If I try to use below code then also I am getting the error
[label,scores] = predict(mdlCv.Trained{1},bank_Test);
Error using classreg.learning.internal.table2PredictMatrix>makeXMatrix (line 97)
Table variable job is not a valid predictor.
Error in classreg.learning.internal.table2PredictMatrix (line 47)
Xout = makeXMatrix(X,CategoricalPredictors,vrange,pnames);
Error in classreg.learning.classif.CompactClassificationTree/predict (line 894)
X = classreg.learning.internal.table2PredictMatrix(X,[],[],...
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Accepted Answer
Walter Roberson
on 6 Dec 2020
Edited: Walter Roberson
on 6 Dec 2020
For cross-validated classification trees, create a classification partitioned object, https://www.mathworks.com/help/stats/classreg.learning.partition.classificationpartitionedmodel-class.html using fitctree https://www.mathworks.com/help/stats/fitctree.html#bt6cr7t-tree and use kfoldPredict https://www.mathworks.com/help/stats/classificationpartitionedmodel.kfoldpredict.html
You are using 'KFold' so you are creating a classification partitioned object and need to use kfoldPredict() instead of predict()
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