MATLAB: What does it mean: “MaxNumSplits is not a valid parameter name”

ens-maxnumsplits

I use follow code in command line

templ = templateTree('MaxNumSplits',4);

ens = fitensemble(X,Y,'LSBoost',50,templ);

And got follow message:

Error using classreg.learning.FitTemplate/fillIfNeeded (line 533)

MaxNumSplits is not a valid parameter name.

Error in classreg.learning.modelparams.EnsembleParams/fillDefaultParams (line 622)

          learner = fillIfNeeded(learner,this.Type);

I don't know why MaxNumSplits is not a valid parameter name?

who can explain it? thank you ….

Best Answer

I believe the Matlab version that you are using does not support MaxNumSplits. To check this, type

help templateTree

and see whether you can find the parameter name "MaxNumSplits" or not.

Related Solutions

MATLAB: LIBSVM: MATLAB function “trainsvm” deprecated and replaced by “fitcsvm”

You have

fitcsvm(TrainLabels, K, '-t 4')

The valid syntaxes for fitcsvm are

Mdl = fitcsvm(Tbl,ResponseVarName)
Mdl = fitcsvm(Tbl,formula)
Mdl = fitcsvm(Tbl,Y)
Mdl = fitcsvm(X,Y)
Mdl = fitcsvm(___,Name,Value)

The only one of this that permits a third parameter is the last one, with name/value pairs. But '-t 4' is not a name/value pair.

MATLAB's svmtrain() also never supported that syntax. That syntax was valid only for libsvm . You can still pull in libsvm if you want to.

The syntax for fitcsvm never has the label information as a separate first parameter, only integrated as part of the first parameter, or as a second parameter.

You should be having a look at the KernelFunction option

MATLAB: Adding parameter and value pair in fitcdiscr for QDA classifier

Passing KFold to any classification model creates a ClassificationPartitionedModel, which is a set of multiple models. As such, one cannot call predict on it. Instead, you can use kfoldPredict function, which gives you the classification accuracy on the training partition for each of the model. For example,

load fisheriris.mat
mdl = fitcdiscr(meas, species, 'DiscrimType', 'quadratic', 'KFold', 4);
kfoldPredict(mdl)

If you want to predict on test dataset, train a separate model with same parameters, except for KFold, and then use predict on the test dataset.

The error message is a known issue, and might be fixed in any of the upcoming releases.

Best Answer

Related Solutions

MATLAB: LIBSVM: MATLAB function “trainsvm” deprecated and replaced by “fitcsvm”

MATLAB: Adding parameter and value pair in fitcdiscr for QDA classifier

Related Question