Check for missing argument or incorrect argument data type in call to function 'predict'.
5 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
Tejal Mehta
el 6 de Dic. de 2020
Comentada: Tejal Mehta
el 6 de Dic. de 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,[],[],...
0 comentarios
Respuesta aceptada
Walter Roberson
el 6 de Dic. de 2020
Editada: Walter Roberson
el 6 de Dic. de 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()
Más respuestas (0)
Ver también
Productos
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!