How to solve an error using a function generated by Classification Learner

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I am using a function that was generated in the Classification Learner App:
function [trainedClassifierLinDisLinDis, validationAccuracy] = trainClassifierLinDis(trainingData)
% [trainedClassifierLinDis, validationAccuracy] = trainClassifier(trainingData)
% Returns a trained classifier and its accuracy. This code recreates the
% classification model trained in Classification Learner app. Use the
% generated code to automate training the same model with new data, or to
% learn how to programmatically train models.
%
% Input:
% trainingData: A table containing the same predictor and response
% columns as those imported into the app.
%
% Output:
% trainedClassifierLinDis: A struct containing the trained classifier. The
% struct contains various fields with information about the trained
% classifier.
%
% trainedClassifierLinDis.predictFcn: A function to make predictions on new
% data.
%
% validationAccuracy: A double containing the accuracy in percent. In
% the app, the History list displays this overall accuracy score for
% each model.
%
% Use the code to train the model with new data. To retrain your
% classifier, call the function from the command line with your original
% data or new data as the input argument trainingData.
%
% For example, to retrain a classifier trained with the original data set
% T, enter:
% [trainedClassifierLinDis, validationAccuracy] = trainClassifierLinDis(T)
%
% To make predictions with the returned 'trainedClassifierLinDis' on new data T2,
% use
% yfit = trainedClassifierLinDis.predictFcn(T2)
%
% T2 must be a table containing at least the same predictor columns as used
% during training. For details, enter:
% trainedClassifierLinDis.HowToPredict
% Auto-generated by MATLAB on 30-Aug-2021 16:11:50
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'scenedata1', 'scenedata2', 'scenedata3', 'scenedata4', 'scenedata5', 'scenedata6', 'scenedata7', 'scenedata8', 'scenedata9', 'scenedata10', 'scenedata11', 'scenedata12', 'scenedata13', 'scenedata14', 'scenedata15', 'scenedata16', 'scenedata17', 'scenedata18', 'scenedata19', 'scenedata20', 'scenedata21', 'scenedata22', 'scenedata23', 'scenedata24', 'scenedata25', 'scenedata26', 'scenedata27', 'scenedata28', 'scenedata29', 'scenedata30', 'scenedata31', 'scenedata32', 'scenedata33', 'scenedata34', 'scenedata35', 'scenedata36', 'scenedata37', 'scenedata38', 'scenedata39', 'scenedata40', 'scenedata41', 'scenedata42', 'scenedata43', 'scenedata44', 'scenedata45', 'scenedata46', 'scenedata47', 'scenedata48', 'scenedata49', 'scenedata50', 'scenedata51', 'scenedata52', 'scenedata53', 'scenedata54', 'scenedata55', 'scenedata56', 'scenedata57', 'scenedata58', 'scenedata59', 'scenedata60', 'scenedata61', 'scenedata62', 'scenedata63', 'scenedata64', 'scenedata65', 'scenedata66', 'scenedata67', 'scenedata68', 'scenedata69', 'scenedata70', 'scenedata71', 'scenedata72', 'scenedata73', 'scenedata74', 'scenedata75', 'scenedata76', 'scenedata77', 'scenedata78', 'scenedata79', 'scenedata80', 'scenedata81', 'scenedata82', 'scenedata83', 'scenedata84', 'scenedata85', 'scenedata86', 'scenedata87', 'scenedata88', 'scenedata89', 'scenedata90', 'scenedata91', 'scenedata92', 'scenedata93', 'scenedata94', 'scenedata95', 'scenedata96', 'scenedata97', 'scenedata98', 'scenedata99', 'scenedata100', 'scenedata101', 'scenedata102', 'scenedata103', 'scenedata104', 'scenedata105', 'scenedata106', 'scenedata107', 'scenedata108', 'scenedata109', 'scenedata110', 'scenedata111', 'scenedata112', 'scenedata113', 'scenedata114', 'scenedata115', 'scenedata116', 'scenedata117', 'scenedata118', 'scenedata119', 'scenedata120', 'scenedata121', 'scenedata122', 'scenedata123', 'scenedata124', 'scenedata125', 'scenedata126', 'scenedata127', 'scenedata128', 'scenedata129', 'scenedata130', 'scenedata131', 'scenedata132', 'scenedata133', 'scenedata134', 'scenedata135', 'scenedata136', 'scenedata137', 'scenedata138', 'scenedata139', 'scenedata140', 'scenedata141', 'scenedata142', 'scenedata143', 'scenedata144', 'scenedata145', 'scenedata146', 'scenedata147', 'scenedata148', 'scenedata149', 'scenedata150', 'scenedata151', 'scenedata152', 'scenedata153', 'scenedata154', 'scenedata155', 'scenedata156', 'scenedata157', 'scenedata158', 'scenedata159', 'scenedata160', 'scenedata161', 'scenedata162', 'scenedata163', 'scenedata164', 'scenedata165', 'scenedata166', 'scenedata167', 'scenedata168', 'scenedata169', 'scenedata170', 'scenedata171', 'scenedata172', 'scenedata173', 'scenedata174', 'scenedata175', 'scenedata176', 'scenedata177', 'scenedata178', 'scenedata179', 'scenedata180', 'scenedata181', 'scenedata182', 'scenedata183', 'scenedata184', 'scenedata185', 'scenedata186', 'scenedata187', 'scenedata188', 'scenedata189', 'scenedata190', 'scenedata191', 'scenedata192', 'scenedata193', 'scenedata194', 'scenedata195', 'scenedata196', 'scenedata197', 'scenedata198', 'scenedata199', 'scenedata200', 'scenedata201', 'scenedata202', 'scenedata203', 'scenedata204', 'scenedata205', 'scenedata206', 'scenedata207', 'scenedata208', 'scenedata209', 'scenedata210', 'scenedata211', 'scenedata212', 'scenedata213', 'scenedata214', 'scenedata215', 'scenedata216', 'scenedata217', 'scenedata218', 'scenedata219', 'scenedata220', 'scenedata221', 'scenedata222', 'scenedata223', 'scenedata224', 'scenedata225', 'scenedata226', 'scenedata227', 'scenedata228', 'scenedata229', 'scenedata230', 'scenedata231', 'scenedata232', 'scenedata233', 'scenedata234', 'scenedata235', 'scenedata236', 'scenedata237', 'scenedata238', 'scenedata239', 'scenedata240', 'scenedata241', 'scenedata242', 'scenedata243', 'scenedata244', 'scenedata245', 'scenedata246', 'scenedata247', 'scenedata248', 'scenedata249', 'scenedata250'};
predictors = inputTable(:, predictorNames);
response = inputTable.sceneType;
isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];
% Train a classifier
% This code specifies all the classifier options and trains the classifier.
classificationDiscriminant = fitcdiscr(...
predictors, ...
response, ...
'DiscrimType', 'linear', ...
'Gamma', 0, ...
'FillCoeffs', 'off', ...
'ClassNames', categorical({'MS T1 No Contrast'; 'MS T1 w: contrast'; 'Normal T1 No Contrast'; 'Normal T1 w:contrast'; 'T2 MS No Contrast'; 'T2 Normal No Contrast'}));
% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
discriminantPredictFcn = @(x) predict(classificationDiscriminant, x);
trainedClassifierLinDis.predictFcn = @(x) discriminantPredictFcn(predictorExtractionFcn(x));
% Add additional fields to the result struct
trainedClassifierLinDis.RequiredVariables = {'scenedata1', 'scenedata10', 'scenedata100', 'scenedata101', 'scenedata102', 'scenedata103', 'scenedata104', 'scenedata105', 'scenedata106', 'scenedata107', 'scenedata108', 'scenedata109', 'scenedata11', 'scenedata110', 'scenedata111', 'scenedata112', 'scenedata113', 'scenedata114', 'scenedata115', 'scenedata116', 'scenedata117', 'scenedata118', 'scenedata119', 'scenedata12', 'scenedata120', 'scenedata121', 'scenedata122', 'scenedata123', 'scenedata124', 'scenedata125', 'scenedata126', 'scenedata127', 'scenedata128', 'scenedata129', 'scenedata13', 'scenedata130', 'scenedata131', 'scenedata132', 'scenedata133', 'scenedata134', 'scenedata135', 'scenedata136', 'scenedata137', 'scenedata138', 'scenedata139', 'scenedata14', 'scenedata140', 'scenedata141', 'scenedata142', 'scenedata143', 'scenedata144', 'scenedata145', 'scenedata146', 'scenedata147', 'scenedata148', 'scenedata149', 'scenedata15', 'scenedata150', 'scenedata151', 'scenedata152', 'scenedata153', 'scenedata154', 'scenedata155', 'scenedata156', 'scenedata157', 'scenedata158', 'scenedata159', 'scenedata16', 'scenedata160', 'scenedata161', 'scenedata162', 'scenedata163', 'scenedata164', 'scenedata165', 'scenedata166', 'scenedata167', 'scenedata168', 'scenedata169', 'scenedata17', 'scenedata170', 'scenedata171', 'scenedata172', 'scenedata173', 'scenedata174', 'scenedata175', 'scenedata176', 'scenedata177', 'scenedata178', 'scenedata179', 'scenedata18', 'scenedata180', 'scenedata181', 'scenedata182', 'scenedata183', 'scenedata184', 'scenedata185', 'scenedata186', 'scenedata187', 'scenedata188', 'scenedata189', 'scenedata19', 'scenedata190', 'scenedata191', 'scenedata192', 'scenedata193', 'scenedata194', 'scenedata195', 'scenedata196', 'scenedata197', 'scenedata198', 'scenedata199', 'scenedata2', 'scenedata20', 'scenedata200', 'scenedata201', 'scenedata202', 'scenedata203', 'scenedata204', 'scenedata205', 'scenedata206', 'scenedata207', 'scenedata208', 'scenedata209', 'scenedata21', 'scenedata210', 'scenedata211', 'scenedata212', 'scenedata213', 'scenedata214', 'scenedata215', 'scenedata216', 'scenedata217', 'scenedata218', 'scenedata219', 'scenedata22', 'scenedata220', 'scenedata221', 'scenedata222', 'scenedata223', 'scenedata224', 'scenedata225', 'scenedata226', 'scenedata227', 'scenedata228', 'scenedata229', 'scenedata23', 'scenedata230', 'scenedata231', 'scenedata232', 'scenedata233', 'scenedata234', 'scenedata235', 'scenedata236', 'scenedata237', 'scenedata238', 'scenedata239', 'scenedata24', 'scenedata240', 'scenedata241', 'scenedata242', 'scenedata243', 'scenedata244', 'scenedata245', 'scenedata246', 'scenedata247', 'scenedata248', 'scenedata249', 'scenedata25', 'scenedata250', 'scenedata26', 'scenedata27', 'scenedata28', 'scenedata29', 'scenedata3', 'scenedata30', 'scenedata31', 'scenedata32', 'scenedata33', 'scenedata34', 'scenedata35', 'scenedata36', 'scenedata37', 'scenedata38', 'scenedata39', 'scenedata4', 'scenedata40', 'scenedata41', 'scenedata42', 'scenedata43', 'scenedata44', 'scenedata45', 'scenedata46', 'scenedata47', 'scenedata48', 'scenedata49', 'scenedata5', 'scenedata50', 'scenedata51', 'scenedata52', 'scenedata53', 'scenedata54', 'scenedata55', 'scenedata56', 'scenedata57', 'scenedata58', 'scenedata59', 'scenedata6', 'scenedata60', 'scenedata61', 'scenedata62', 'scenedata63', 'scenedata64', 'scenedata65', 'scenedata66', 'scenedata67', 'scenedata68', 'scenedata69', 'scenedata7', 'scenedata70', 'scenedata71', 'scenedata72', 'scenedata73', 'scenedata74', 'scenedata75', 'scenedata76', 'scenedata77', 'scenedata78', 'scenedata79', 'scenedata8', 'scenedata80', 'scenedata81', 'scenedata82', 'scenedata83', 'scenedata84', 'scenedata85', 'scenedata86', 'scenedata87', 'scenedata88', 'scenedata89', 'scenedata9', 'scenedata90', 'scenedata91', 'scenedata92', 'scenedata93', 'scenedata94', 'scenedata95', 'scenedata96', 'scenedata97', 'scenedata98', 'scenedata99'};
trainedClassifierLinDis.ClassificationDiscriminant = classificationDiscriminant;
trainedClassifierLinDis.About = 'This struct is a trained model exported from Classification Learner R2020a.';
trainedClassifierLinDis.HowToPredict = sprintf('To make predictions on a new table, T, use: \n yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nThe table, T, must contain the variables returned by: \n c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see <a href="matlab:helpview(fullfile(docroot, ''stats'', ''stats.map''), ''appclassification_exportmodeltoworkspace'')">How to predict using an exported model</a>.');
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'scenedata1', 'scenedata2', 'scenedata3', 'scenedata4', 'scenedata5', 'scenedata6', 'scenedata7', 'scenedata8', 'scenedata9', 'scenedata10', 'scenedata11', 'scenedata12', 'scenedata13', 'scenedata14', 'scenedata15', 'scenedata16', 'scenedata17', 'scenedata18', 'scenedata19', 'scenedata20', 'scenedata21', 'scenedata22', 'scenedata23', 'scenedata24', 'scenedata25', 'scenedata26', 'scenedata27', 'scenedata28', 'scenedata29', 'scenedata30', 'scenedata31', 'scenedata32', 'scenedata33', 'scenedata34', 'scenedata35', 'scenedata36', 'scenedata37', 'scenedata38', 'scenedata39', 'scenedata40', 'scenedata41', 'scenedata42', 'scenedata43', 'scenedata44', 'scenedata45', 'scenedata46', 'scenedata47', 'scenedata48', 'scenedata49', 'scenedata50', 'scenedata51', 'scenedata52', 'scenedata53', 'scenedata54', 'scenedata55', 'scenedata56', 'scenedata57', 'scenedata58', 'scenedata59', 'scenedata60', 'scenedata61', 'scenedata62', 'scenedata63', 'scenedata64', 'scenedata65', 'scenedata66', 'scenedata67', 'scenedata68', 'scenedata69', 'scenedata70', 'scenedata71', 'scenedata72', 'scenedata73', 'scenedata74', 'scenedata75', 'scenedata76', 'scenedata77', 'scenedata78', 'scenedata79', 'scenedata80', 'scenedata81', 'scenedata82', 'scenedata83', 'scenedata84', 'scenedata85', 'scenedata86', 'scenedata87', 'scenedata88', 'scenedata89', 'scenedata90', 'scenedata91', 'scenedata92', 'scenedata93', 'scenedata94', 'scenedata95', 'scenedata96', 'scenedata97', 'scenedata98', 'scenedata99', 'scenedata100', 'scenedata101', 'scenedata102', 'scenedata103', 'scenedata104', 'scenedata105', 'scenedata106', 'scenedata107', 'scenedata108', 'scenedata109', 'scenedata110', 'scenedata111', 'scenedata112', 'scenedata113', 'scenedata114', 'scenedata115', 'scenedata116', 'scenedata117', 'scenedata118', 'scenedata119', 'scenedata120', 'scenedata121', 'scenedata122', 'scenedata123', 'scenedata124', 'scenedata125', 'scenedata126', 'scenedata127', 'scenedata128', 'scenedata129', 'scenedata130', 'scenedata131', 'scenedata132', 'scenedata133', 'scenedata134', 'scenedata135', 'scenedata136', 'scenedata137', 'scenedata138', 'scenedata139', 'scenedata140', 'scenedata141', 'scenedata142', 'scenedata143', 'scenedata144', 'scenedata145', 'scenedata146', 'scenedata147', 'scenedata148', 'scenedata149', 'scenedata150', 'scenedata151', 'scenedata152', 'scenedata153', 'scenedata154', 'scenedata155', 'scenedata156', 'scenedata157', 'scenedata158', 'scenedata159', 'scenedata160', 'scenedata161', 'scenedata162', 'scenedata163', 'scenedata164', 'scenedata165', 'scenedata166', 'scenedata167', 'scenedata168', 'scenedata169', 'scenedata170', 'scenedata171', 'scenedata172', 'scenedata173', 'scenedata174', 'scenedata175', 'scenedata176', 'scenedata177', 'scenedata178', 'scenedata179', 'scenedata180', 'scenedata181', 'scenedata182', 'scenedata183', 'scenedata184', 'scenedata185', 'scenedata186', 'scenedata187', 'scenedata188', 'scenedata189', 'scenedata190', 'scenedata191', 'scenedata192', 'scenedata193', 'scenedata194', 'scenedata195', 'scenedata196', 'scenedata197', 'scenedata198', 'scenedata199', 'scenedata200', 'scenedata201', 'scenedata202', 'scenedata203', 'scenedata204', 'scenedata205', 'scenedata206', 'scenedata207', 'scenedata208', 'scenedata209', 'scenedata210', 'scenedata211', 'scenedata212', 'scenedata213', 'scenedata214', 'scenedata215', 'scenedata216', 'scenedata217', 'scenedata218', 'scenedata219', 'scenedata220', 'scenedata221', 'scenedata222', 'scenedata223', 'scenedata224', 'scenedata225', 'scenedata226', 'scenedata227', 'scenedata228', 'scenedata229', 'scenedata230', 'scenedata231', 'scenedata232', 'scenedata233', 'scenedata234', 'scenedata235', 'scenedata236', 'scenedata237', 'scenedata238', 'scenedata239', 'scenedata240', 'scenedata241', 'scenedata242', 'scenedata243', 'scenedata244', 'scenedata245', 'scenedata246', 'scenedata247', 'scenedata248', 'scenedata249', 'scenedata250'};
predictors = inputTable(:, predictorNames);
response = inputTable.sceneType;
isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];
% Perform cross-validation
partitionedModel = crossval(trainedClassifierLinDis.ClassificationDiscriminant, 'KFold', 5);
% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');
I am trying to retrain this classifier. When I type in
trainClassifierLinDis(SceneImageData)
which was my original data set, I get this error:
Warning: One or more of the unique class values in GROUP is not present in one or
more folds. For classification problems, either remove this class from the data or
use N instead of GROUP to obtain nonstratified partitions. For regression problems
with continuous response, use N.
> In internal.stats.cvpartitionImpl>stra_kfoldcv (line 364)
In internal.stats/cvpartitionImpl/rerandom (line 315)
In internal.stats.cvpartitionInMemoryImpl (line 166)
In cvpartition (line 175)
In classreg.learning.generator.Partitioner (line 54)
In classreg.learning.modelparams/EnsembleParams/makeGenerator (line 374)
In classreg.learning.modelparams/EnsembleParams/fillDefaultParams (line 628)
In classreg.learning.modelparams/ModelParams/fillIfNeeded (line 93)
In classreg.learning.classif/FullClassificationModel (line 35)
In classreg.learning.classif/ClassificationEnsemble (line 70)
In classreg.learning.partition.ClassificationPartitionedModel (line 161)
In classreg.learning/FitTemplate/fit (line 263)
In ClassificationDiscriminant/crossval (line 656)
In trainClassifierLinDis (line 83)
Warning: Predictor scenedata28 has zero within-class variance. Either exclude this
predictor or set 'discrimType' to 'pseudoLinear' or 'diagLinear'.
> In classreg.learning.ensemble/Ensemble/fitWeakLearners (line 184)
In classreg.learning.classif/ClassificationEnsemble/fitEnsemble (line 369)
In classreg.learning.classif/ClassificationEnsemble (line 79)
In classreg.learning.partition.ClassificationPartitionedModel (line 161)
In classreg.learning/FitTemplate/fit (line 263)
In ClassificationDiscriminant/crossval (line 656)
In trainClassifierLinDis (line 83)
Can someone please clarify the issue? Thank you so much!

Answers (1)

Drew
Drew on 20 Oct 2022
Based on the warning messages, it looks like there are a couple of issues in your dataset.
(1) The first warning sounds like you have an output class label listed at categorical({'MS T1 No Contrast'; 'MS T1 w: contrast'; 'Normal T1 No Contrast'; 'Normal T1 w:contrast'; 'T2 MS No Contrast'; 'T2 Normal No Contrast'}))
but which never occurs in the dataset.
(2) The second warning sounds like predictor scendata28 is a constant, since it has "zero within-class variance"
So, the solution is to:
(1) Remove output class for which there is no data, or get data for it
(2) Remove the predictor scenedata28, or get real (non-constant) data for it.

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