How to resolve the loss function error?

Question

Sanchit 2023-7-25

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评论： the cyclist 2023-7-28

I am trying to use this below matlab function.

figure
plot(loss(bag,Xtest,Ytest,'mode','cumulative'))
xlabel('Number of trees')
ylabel('Test classification error')

But this is giving some errors as follows

Error using classreg.learning.internal.classCount

You passed an unknown class '19.746' of type double.

Error in classreg.learning.classif.ClassificationModel/cleanRows (line 271)

C = classreg.learning.internal.classCount(this.ClassSummary.ClassNames,Y);

Error in classreg.learning.classif.ClassificationModel/prepareDataForLoss (line 364)

[X,C,W,Y,rowData] = cleanRows(this,X,Y,W,rowData,obsInRows);

Error in classreg.learning.classif.CompactClassificationEnsemble/loss (line 388)

[X,C,W] = prepareDataForLoss(this,X,Y,W,[],true,true);

Error in Test_RF_Classify (line 25)

plot(loss(bag,Xtest,Ytest,'mode','cumulative'))

I request you to kindly suggest me how to resolve it. I am attaching the code alongwith input file.

I will appreciate your kind help.

Sanchit

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Answer 1

the cyclist 2023-7-25

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编辑：the cyclist 2023-7-25

There are a number of potential errors with your code, but I would say that the most fundamental one is that you are using a classification algorithm, but you have a numerical response variable.

Classification algorithms are used to predict categorical or nominal variables (e.g. "Prefers to watch Barbie" vs. "Prefers to watch Oppenheimer").

[The specific MATLAB error arises because the category '19.746' did not appear in the training set, so it cannot appear in the test set.]

You might try to use fitrensemble to fit a regression model instead. Your code will actually run to completion if you do so. But I did not look at your code enough to see if the results would be sensible.

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the cyclist 2023-7-26

在 MATLAB Online 中打开

sample.csv

data = readmatrix('sample.csv'); % I used readmatrix instead of readtable

X = data(:, 1:end-1);

Y = data(:, end);

% Redefine Y as requested

Y90 = prctile(Y,90);

Y(Y <= Y90) = 1;

Y(Y > Y90) = 2;

numGroundTruth = numel(Y);

numTrainingSamples = round(0.7 * numGroundTruth);

trainingIndexes = randsample(numGroundTruth, numTrainingSamples);

testIndexes = setdiff((1:numGroundTruth)', trainingIndexes);

rng(1); % For reproducibility

Xtrain = X(trainingIndexes, :);

Xtest = X(testIndexes, :);

Ytrain = Y(trainingIndexes, :);

Ytest = Y(testIndexes, :);

%Create a bagged classification ensemble of 200 trees from the training data.

t = templateTree('Reproducible',true); % For reproducibility of random predictor selections

bag = fitcensemble(Xtrain,Ytrain,'Method','Bag','NumLearningCycles',200,'Learners',t)

bag =

ClassificationBaggedEnsemble ResponseName: 'Y' CategoricalPredictors: [] ClassNames: [1 2] ScoreTransform: 'none' NumObservations: 32 NumTrained: 200 Method: 'Bag' LearnerNames: {'Tree'} ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.' FitInfo: [] FitInfoDescription: 'None' FResample: 1 Replace: 1 UseObsForLearner: [32×200 logical] Properties, Methods

%Plot the loss (misclassification) of the test data as a function of the number of trained trees in the ensemble.

figure

plot(loss(bag,Xtest,Ytest,'mode','cumulative'))

xlabel('Number of trees')

ylabel('Test classification error')

%Cross Validation, Generate a five-fold cross-validated bagged ensemble.

cv = fitcensemble(X,Y,'Method','Bag','NumLearningCycles',200,'Kfold',5,'Learners',t)

cv =

ClassificationPartitionedEnsemble CrossValidatedModel: 'Bag' PredictorNames: {'x1' 'x2' 'x3' 'x4' 'x5' 'x6' 'x7' 'x8' 'x9'} ResponseName: 'Y' NumObservations: 46 KFold: 5 Partition: [1×1 cvpartition] NumTrainedPerFold: [200 200 200 200 200] ClassNames: [1 2] ScoreTransform: 'none' Properties, Methods

%Examine the cross-validation loss as a function of the number of trees in the ensemble.

figure

plot(loss(bag,Xtest,Ytest,'mode','cumulative'))

hold on

plot(kfoldLoss(cv,'mode','cumulative'),'r.')

hold off

xlabel('Number of trees')

ylabel('Classification error')

legend('Test','Cross-validation','Location','NE')

%Out-of-Bag Estimates

%Generate the loss curve for out-of-bag estimates, and plot it along with the other curves.

figure

plot(loss(bag,Xtest,Ytest,'mode','cumulative'))

hold on

plot(kfoldLoss(cv,'mode','cumulative'),'r.')

plot(oobLoss(bag,'mode','cumulative'),'k--')

hold off

xlabel('Number of trees')

ylabel('Classification error')

legend('Test','Cross-validation','Out of bag','Location','NE')

%The out-of-bag estimates are again comparable to those of the other methods.

Sanchit 2023-7-28

编辑：Sanchit 2023-7-28

Thank you very much for your kind help. In an another problem, I want to compute the daily mean of 8 variables over Lat x Lon x Time. My netcdf file contains these 8 variables for 03,06,09 and 12 GMT observations over 9 years data. I am attaching the matlab code with input file. I request to please have a look on code and suggest me how to fix it.

I would be grateful to you for your kind help.

Sanchit

the cyclist 2023-7-28

You should open a new question for that, and accept the answer here if you found it helpful.

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How to resolve the loss function error?

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How to resolve the loss function error?

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