How can i calculate training and testing time of a model using classification learner app?

I am using wine dataset for experiment

    % load wine_dataset
    % [dummy, Target] = max(wineTargets);
    % wineDataset = [wineInputs; Target];

I imported wineDataset matrix in classification learner app and train the data using Linear SVM and Quadratic SVM. How can I calculate training and testing time taken by model?

Answer 1

[Image Analyst]

Export the code, and then use tic and toc.

[VIJENDRA]

function [trainedClassifier, validationAccuracy] = trainClassifier(datasetTable)
% Convert input to table
datasetTable = table(datasetTable');
datasetTable.Properties.VariableNames = {'row'};
% Split matrices in the input table into vectors
datasetTable.row_1 = datasetTable.row(:,1);
datasetTable.row_2 = datasetTable.row(:,2);
datasetTable.row_3 = datasetTable.row(:,3);
datasetTable.row_4 = datasetTable.row(:,4);
datasetTable.row_5 = datasetTable.row(:,5);
datasetTable.row_6 = datasetTable.row(:,6);
datasetTable.row_7 = datasetTable.row(:,7);
datasetTable.row_8 = datasetTable.row(:,8);
datasetTable.row_9 = datasetTable.row(:,9);
datasetTable.row_10 = datasetTable.row(:,10);
datasetTable.row_11 = datasetTable.row(:,11);
datasetTable.row_12 = datasetTable.row(:,12);
datasetTable.row_13 = datasetTable.row(:,13);
datasetTable.row_14 = datasetTable.row(:,14);
datasetTable.row = [];
% Extract predictors and response
predictorNames = {'row_1', 'row_2', 'row_3', 'row_4', 'row_5', 'row_6', 'row_7', 'row_8', 'row_9', 'row_10', 'row_11', 'row_12', 'row_13'};
predictors = datasetTable(:,predictorNames);
predictors = table2array(varfun(@double, predictors));
response = datasetTable.row_14;
% Train a classifier
template = templateSVM('KernelFunction', 'linear', 'PolynomialOrder', [], 'KernelScale', 'auto', 'BoxConstraint', 1, 'Standardize', 1);
trainedClassifier = fitcecoc(predictors, response, 'Learners', template, 'Coding', 'onevsone', 'PredictorNames', {'row_1' 'row_2' 'row_3' 'row_4' 'row_5' 'row_6' 'row_7' 'row_8' 'row_9' 'row_10' 'row_11' 'row_12' 'row_13'}, 'ResponseName', 'row_14', 'ClassNames', [1 2 3]);

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

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

%%Uncomment this section to compute validation predictions and scores:
% % Compute validation predictions and scores
% [validationPredictions, validationScores] = kfoldPredict(partitionedModel);

Where should i put tic and toc as 'fitcecoc' function using all data i.e. 178 observations to train the model?

[Image Analyst]

Put tic where you want to start the stop watch, and put toc where you want to stop the stop watch and discover the elapsed time.

[Paul Ho]

I know this is an old post, but I can't seem to find where I can export MATLAB code from the classification learner app. (Of course I can just copy, paste and adapt the above, but it would be nice to know how it was generated)

If anyone could link me to any useful articles/ docs I would appreciate that too.