How can I get my data set through matlab code generated by MATLAB classification learner app?

I trained my model and generated MATLAB code through classification learner app present in MATLAB. I wish to retrain the model on some new data set through the generated MATLAB code.i have not done any changes to the code but Still , I'm getting error. Can anyone help me to correct this ?

>> trainClassifier
Not enough input arguments.

Error in trainClassifier (line 52)
inputTable = array2table(trainingData, 'VariableNames', {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10',
'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23',
'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36',
'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49',
'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62',
'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75',
'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88',
'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96', 'column_97', 'column_98', 'column_99', 'column_100', 'column_101',
'column_102', 'column_103', 'column_104', 'column_105', 'column_106', 'column_107', 'column_108', 'column_109', 'column_110', 'column_111', 'column_112', 'column_113',
'column_114', 'column_115', 'column_116', 'column_117', 'column_118', 'column_119', 'column_120', 'column_121', 'column_122', 'column_123', 'column_124', 'column_125',
'column_126', 'column_127', 'column_128', 'column_129', 'column_130', 'column_131', 'column_132', 'column_133', 'column_134', 'column_135', 'column_136', 'column_137',
'column_138', 'column_139', 'column_140', 'column_141', 'column_142', 'column_143', 'column_144', 'column_145', 'column_146', 'column_147', 'column_148', 'column_149',
'column_150', 'column_151', 'column_152', 'column_153', 'column_154', 'column_155', 'column_156', 'column_157', 'column_158', 'column_159', 'column_160', 'column_161',
'column_162', 'column_163', 'column_164', 'column_165', 'column_166', 'column_167', 'column_168', 'column_169', 'column_170', 'column_171', 'column_172', 'column_173',
'column_174', 'column_175', 'column_176', 'column_177', 'column_178', 'column_179', 'column_180', 'column_181', 'column_182', 'column_183', 'column_184', 'column_185',
'column_186', 'column_187', 'column_188', 'column_189', 'column_190', 'column_191', 'column_192', 'column_193', 'column_194', 'column_195', 'column_196', 'column_197',
'column_198', 'column_199', 'column_200', 'column_201', 'column_202'});

function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData, responseData)
% [trainedClassifier, validationAccuracy] = trainClassifier(trainingData,
% responseData)
% 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 matrix with the same number of columns and data type
%       as the matrix imported into the app.
%
%      responseData: A vector with the same data type as the vector
%       imported into the app. The length of responseData and the number of
%       rows of trainingData must be equal.
%
%  Output:
%      trainedClassifier: A struct containing the trained classifier. The
%       struct contains various fields with information about the trained
%       classifier.
%
%      trainedClassifier.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 arguments trainingData and responseData.
%
% For example, to retrain a classifier trained with the original data set T
% and response Y, enter:
%   [trainedClassifier, validationAccuracy] = trainClassifier(T, Y)
%
% To make predictions with the returned 'trainedClassifier' on new data T2,
% use
%   yfit = trainedClassifier.predictFcn(T2)
%
% T2 must be a matrix containing only the predictor columns used for
% training. For details, enter:
%   trainedClassifier.HowToPredict

% Auto-generated by MATLAB on 18-Apr-2021 21:06:26


% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
% Convert input to table
inputTable = array2table(trainingData, 'VariableNames', {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96', 'column_97', 'column_98', 'column_99', 'column_100', 'column_101', 'column_102', 'column_103', 'column_104', 'column_105', 'column_106', 'column_107', 'column_108', 'column_109', 'column_110', 'column_111', 'column_112', 'column_113', 'column_114', 'column_115', 'column_116', 'column_117', 'column_118', 'column_119', 'column_120', 'column_121', 'column_122', 'column_123', 'column_124', 'column_125', 'column_126', 'column_127', 'column_128', 'column_129', 'column_130', 'column_131', 'column_132', 'column_133', 'column_134', 'column_135', 'column_136', 'column_137', 'column_138', 'column_139', 'column_140', 'column_141', 'column_142', 'column_143', 'column_144', 'column_145', 'column_146', 'column_147', 'column_148', 'column_149', 'column_150', 'column_151', 'column_152', 'column_153', 'column_154', 'column_155', 'column_156', 'column_157', 'column_158', 'column_159', 'column_160', 'column_161', 'column_162', 'column_163', 'column_164', 'column_165', 'column_166', 'column_167', 'column_168', 'column_169', 'column_170', 'column_171', 'column_172', 'column_173', 'column_174', 'column_175', 'column_176', 'column_177', 'column_178', 'column_179', 'column_180', 'column_181', 'column_182', 'column_183', 'column_184', 'column_185', 'column_186', 'column_187', 'column_188', 'column_189', 'column_190', 'column_191', 'column_192', 'column_193', 'column_194', 'column_195', 'column_196', 'column_197', 'column_198', 'column_199', 'column_200', 'column_201', 'column_202'});

predictorNames = {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96', 'column_97', 'column_98', 'column_99', 'column_100', 'column_101', 'column_102', 'column_103', 'column_104', 'column_105', 'column_106', 'column_107', 'column_108', 'column_109', 'column_110', 'column_111', 'column_112', 'column_113', 'column_114', 'column_115', 'column_116', 'column_117', 'column_118', 'column_119', 'column_120', 'column_121', 'column_122', 'column_123', 'column_124', 'column_125', 'column_126', 'column_127', 'column_128', 'column_129', 'column_130', 'column_131', 'column_132', 'column_133', 'column_134', 'column_135', 'column_136', 'column_137', 'column_138', 'column_139', 'column_140', 'column_141', 'column_142', 'column_143', 'column_144', 'column_145', 'column_146', 'column_147', 'column_148', 'column_149', 'column_150', 'column_151', 'column_152', 'column_153', 'column_154', 'column_155', 'column_156', 'column_157', 'column_158', 'column_159', 'column_160', 'column_161', 'column_162', 'column_163', 'column_164', 'column_165', 'column_166', 'column_167', 'column_168', 'column_169', 'column_170', 'column_171', 'column_172', 'column_173', 'column_174', 'column_175', 'column_176', 'column_177', 'column_178', 'column_179', 'column_180', 'column_181', 'column_182', 'column_183', 'column_184', 'column_185', 'column_186', 'column_187', 'column_188', 'column_189', 'column_190', 'column_191', 'column_192', 'column_193', 'column_194', 'column_195', 'column_196', 'column_197', 'column_198', 'column_199', 'column_200', 'column_201', 'column_202'};
predictors = inputTable(:, predictorNames);
response = responseData;
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];

% Train a classifier
% This code specifies all the classifier options and trains the classifier.
% template = templateSVM(...
    'KernelFunction', 'polynomial', ...
    'PolynomialOrder', 2, ...
    'KernelScale', 'auto', ...
    'BoxConstraint', 1, ...
    'Standardize', true);
classificationSVM = fitcecoc(...
    predictors, ...
    response, ...
    'Learners', template, ...
    'Coding', 'onevsone', ...
    'ClassNames', categorical({'blues'; 'classical'; 'country'; 'disco'; 'hiphop'; 'jazz'; 'metal'; 'pop'; 'reggae'; 'rock'}));

% Create the result struct with predict function
predictorExtractionFcn = @(x) array2table(x, 'VariableNames', predictorNames);
svmPredictFcn = @(x) predict(classificationSVM, x);
trainedClassifier.predictFcn = @(x) svmPredictFcn(predictorExtractionFcn(x));

% Add additional fields to the result struct
trainedClassifier.ClassificationSVM = classificationSVM;
trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2021a.';
trainedClassifier.HowToPredict = sprintf('To make predictions on a new predictor column matrix, X, use: \n  yfit = c.predictFcn(X) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nX must contain exactly 202 columns because this model was trained using 202 predictors. \nX must contain only predictor columns in exactly the same order and format as your training \ndata. Do not include the response column or any columns you did not import into the app. \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.
% Convert input to table
inputTable = array2table(trainingData, 'VariableNames', {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96', 'column_97', 'column_98', 'column_99', 'column_100', 'column_101', 'column_102', 'column_103', 'column_104', 'column_105', 'column_106', 'column_107', 'column_108', 'column_109', 'column_110', 'column_111', 'column_112', 'column_113', 'column_114', 'column_115', 'column_116', 'column_117', 'column_118', 'column_119', 'column_120', 'column_121', 'column_122', 'column_123', 'column_124', 'column_125', 'column_126', 'column_127', 'column_128', 'column_129', 'column_130', 'column_131', 'column_132', 'column_133', 'column_134', 'column_135', 'column_136', 'column_137', 'column_138', 'column_139', 'column_140', 'column_141', 'column_142', 'column_143', 'column_144', 'column_145', 'column_146', 'column_147', 'column_148', 'column_149', 'column_150', 'column_151', 'column_152', 'column_153', 'column_154', 'column_155', 'column_156', 'column_157', 'column_158', 'column_159', 'column_160', 'column_161', 'column_162', 'column_163', 'column_164', 'column_165', 'column_166', 'column_167', 'column_168', 'column_169', 'column_170', 'column_171', 'column_172', 'column_173', 'column_174', 'column_175', 'column_176', 'column_177', 'column_178', 'column_179', 'column_180', 'column_181', 'column_182', 'column_183', 'column_184', 'column_185', 'column_186', 'column_187', 'column_188', 'column_189', 'column_190', 'column_191', 'column_192', 'column_193', 'column_194', 'column_195', 'column_196', 'column_197', 'column_198', 'column_199', 'column_200', 'column_201', 'column_202'});

predictorNames = {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96', 'column_97', 'column_98', 'column_99', 'column_100', 'column_101', 'column_102', 'column_103', 'column_104', 'column_105', 'column_106', 'column_107', 'column_108', 'column_109', 'column_110', 'column_111', 'column_112', 'column_113', 'column_114', 'column_115', 'column_116', 'column_117', 'column_118', 'column_119', 'column_120', 'column_121', 'column_122', 'column_123', 'column_124', 'column_125', 'column_126', 'column_127', 'column_128', 'column_129', 'column_130', 'column_131', 'column_132', 'column_133', 'column_134', 'column_135', 'column_136', 'column_137', 'column_138', 'column_139', 'column_140', 'column_141', 'column_142', 'column_143', 'column_144', 'column_145', 'column_146', 'column_147', 'column_148', 'column_149', 'column_150', 'column_151', 'column_152', 'column_153', 'column_154', 'column_155', 'column_156', 'column_157', 'column_158', 'column_159', 'column_160', 'column_161', 'column_162', 'column_163', 'column_164', 'column_165', 'column_166', 'column_167', 'column_168', 'column_169', 'column_170', 'column_171', 'column_172', 'column_173', 'column_174', 'column_175', 'column_176', 'column_177', 'column_178', 'column_179', 'column_180', 'column_181', 'column_182', 'column_183', 'column_184', 'column_185', 'column_186', 'column_187', 'column_188', 'column_189', 'column_190', 'column_191', 'column_192', 'column_193', 'column_194', 'column_195', 'column_196', 'column_197', 'column_198', 'column_199', 'column_200', 'column_201', 'column_202'};
predictors = inputTable(:, predictorNames);
response = responseData;
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];

% Perform cross-validation
partitionedModel = crossval(trainedClassifier.ClassificationSVM, 'KFold', 5);

% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);

% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');

[Ömer Faruk Kilic]

This script has seen to me just as a training script. How can i use this script for my testing dataset? I have test data as well but i didn't understand that how am i supposed to use this script for my testing data.

Answer 1

[Shraddha Jain]

Hi Omer,

Refer to the documentation on Export Classification Model to Predict New Data for more information on using the trainedModel.predictFcn to get prediction results on the test data.

Hope this helps!