Export function Classification Learner App

Hi i am trying to train a classifier based on the function i exported from the classification learner app.

after i have done that, a function appears, but no training seems to happen even after replacing the training data with my new dataset.The code below is not the actual code. I am aware on some things that are missing, i removed them or else i would not be able to post this question

My goal here is to be able to use this function on another device and replicate the hyperparameters i have used in the classification learner app to train the dataset. I tried copying '[trainedClassifier, validationAccuracy] = trainClassifier(trainingData)' into the command window and running it but it just says this. any idea how i can get this function running?

clear,clc;
load('training_set.mat');
trainingData = dataset_train;

function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)
% [trainedClassifier, 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:
%      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 as a
%       percentage. In the app, the Models pane 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:
%   [trainedClassifier, validationAccuracy] = trainClassifier(T)
%
% To make predictions with the returned 'trainedClassifier' on new data T2,
% use
%   yfit = trainedClassifier.predictFcn(T2)
%
% T2 must be a table containing at least the same predictor columns as used
% during training. For details, enter:
%   trainedClassifier.HowToPredict

% Auto-generated by MATLAB on 13-Nov-2022 22:25:12


% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'feature 1',etc...}
predictors = inputTable(:, predictorNames);
response = inputTable.Group;
isCategoricalPredictor = {false, false, etc.....}

% Train a classifier
% This code specifies all the classifier options and trains the classifier.
template = templateSVM(...
    'KernelFunction', 'linear', ...
    'PolynomialOrder', [], ...
    'KernelScale', 'auto', ...
    'BoxConstraint', 1, ...
    'Standardize', true);
classificationSVM = fitcecoc(...
    predictors, ...
    response, ...
    'Learners', template, ...
    'Coding', 'onevsall', ...
    'ClassNames', ['D'; 'E'; 'H'; 'L'; 'O'; 'R'; 'W']);

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

% Add additional fields to the result struct
trainedClassifier.RequiredVariables = {feature 1, etc....}
trainedClassifier.ClassificationSVM = classificationSVM;
trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2022a.';
trainedClassifier.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 = {feature 1, etc....}
predictors = inputTable(:, predictorNames);
response = inputTable.Group;
isCategoricalPredictor = {false, etc...}

% 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');
end

Answer 1

[Image Analyst]

It looks like that Export Function button exported the training code. So do you want to

1. use that code (instead of the Classification Learner App) on a different computer to train a new model that uses a different training set? Or,
2. Export a model created with that training data to classify new data on that, or a different, computer?

If #1 (train on new data) you need to put that function into an m-file called "trainClassifier.m" and then call that function with your new training data:

clear
clc;
% Load a new and different set of training data.
load('training_set.mat');
trainingData = dataset_train;
[trainedClassifier, validationAccuracy] = trainClassifier(trainingData) % This gives the results.

If #2 (classify data not used in training), you need to export the model to the workspace first, then save the variable into a .mat file.

save('trainedClassifier.mat', trainedClassifier);

Then when you call load() on that saved model

s = load('trainedClassifier.mat')

it will give you instructions for how to use the model to classify new data.