5 fold cross validation code for a dataset

I want to split my data set into train set as well as test set using 5 fold cross validation .

Answer 1

[Shubham]

Hi Subhasmita,

In MATLAB, you can perform k-fold cross-validation to split your dataset into training and test sets. In k-fold cross-validation, the dataset is divided into k subsets (folds). The model is trained k times, each time using a different fold as the test set and the remaining folds as the training set.

Here's how you can perform 5-fold cross-validation in MATLAB:

% Load your dataset
load fisheriris % Example dataset
X = meas; % Features
y = species; % Labels

% Define the number of folds
k = 5;

% Create a cross-validation partition
cv = cvpartition(y, 'KFold', k);

% Initialize variable to store accuracy for each fold
accuracy = zeros(k, 1);

for i = 1:k
    % Get the training and test indices for the current fold
    trainIdx = training(cv, i);
    testIdx = test(cv, i);
    
    % Split the data into training and test sets for this fold
    XTrain = X(trainIdx, :);
    yTrain = y(trainIdx, :);
    XTest = X(testIdx, :);
    yTest = y(testIdx, :);
    
    % Train the model on the training set
    model = fitcsvm(XTrain, yTrain);
    
    % Test the model on the test set
    predictions = predict(model, XTest);
    
    % Calculate accuracy for the current fold
    accuracy(i) = sum(predictions == yTest) / length(yTest);
    
    % Display accuracy for the current fold
    fprintf('Fold %d Accuracy: %.2f%%\n', i, accuracy(i) * 100);
end

% Calculate and display the average accuracy across all folds
averageAccuracy = mean(accuracy);
fprintf('Average Accuracy: %.2f%%\n', averageAccuracy * 100);

Explanation

1. Data Loading: We use the fisheriris dataset for demonstration, where X contains the features and y contains the labels.
2. Cross-Validation Partition: We create a 5-fold partition using cvpartition with the option 'KFold', k.
3. Loop Through Folds: For each fold, we:

• Extract training and test indices.
• Split the data into training and test sets.
• Train a support vector machine (SVM) model using fitcsvm.
• Predict on the test set and calculate accuracy.

4. Accuracy Calculation: We calculate and print the accuracy for each fold and the average accuracy across all folds.

Additional Notes

• You can replace fitcsvm with any other classifier that suits your needs.
• Ensure that your dataset is suitable for cross-validation, especially regarding class balance.
• You might also want to explore MATLAB's crossval function, which automates some parts of this process.