Kfold remains 0 after fitting

why does my kfold be set to 0 after beeing trained and set like that?

mdl = fitctree(T,"HH_kW_01",KFold=7);
mdl
%kfLoss = kfoldLoss(mdl)
display("end");

mdl =

ClassificationPartitionedModel

CrossValidatedModel: 'Discriminant'

PredictorNames: {1×62 cell}

ResponseName: 'HH_kW_01'

NumObservations: 35136

KFold: 0

Partition: [1×1 cvpartition]

ClassNames: [0 0.0049 0.0097 0.0292 0.0312 0.0321 0.0331 0.0351 0.0360 … ]

ScoreTransform: 'none'

Properties, Methods

Answer 1

[Piyush Patil]

Hello Huyen,

It seems like the KFOLD property of your ClassificationPartitionedModel object is set to 0. Possible reasons for this to happen could be -

1. The KFOLD argument was not correctly passed to the "fitctree" function when the model was trained. So, to ensure that the KFOLD argument was passed correctly to the "fitctree" function, you can refer to the following documentation - Fitctree Function
2. The KFOLD property was modified after the model was trained or the model was not trained using cross-validation.

If the issue still persists, then please share the relevant code and information about data files. It will allow me to better understand the issue so that I can assist you in resolving it.

[Huyen Vu]

Thanks for your answer. It's not only the problem with fitctree but also with other classifier such as fitcdiscr or fitcensemble. The issue always exists when I try to use cross validation, seems like the Kfold property was changed after beeing trained. So, about the data: 3500x64 table containing time series data about electric power consume.