Hi Sahil,
Yes, you can use fitcauto with cross-validation in MATLAB. While the fitcauto function itself is designed to automatically select the best model and its parameters based on the given data, incorporating cross-validation into the process involves using additional options within the function call.
The fitcauto function primarily focuses on simplifying the model selection process by evaluating different models and their hyperparameters. For cross-validation, specifically, you would use the 'CrossVal' option, setting it to 'on' to perform 10-fold cross-validation by default. If you want to specify the number of folds explicitly, you would first create a cross-validation partition using cvpartition and then use that in your model training process.
Here’s a sample script that demonstrates how to use fitcauto with 10-fold cross-validation:
% Load sample data
load fisheriris
X = meas;
Y = species;
% Create a cross-validation partition to specify the number of folds
c = cvpartition(Y, 'KFold', 10);
% Use fitcauto with the 'CrossVal' option and the partition created
model = fitcauto(X, Y, 'CrossVal', 'on', 'CVPartition', c);
% Display the trained model
disp(model.Trained{1}); % Display the first fold model as an example
% Evaluate the model
kfoldLoss = kfoldLoss(model, 'LossFun', 'ClassifError');
fprintf('10-Fold Cross-Validation Loss: %f\n', kfoldLoss);
In this script:
- cvpartition is used to create a cross-validation partition object c for the dataset, specifying 10 folds with the 'KFold' option.
- fitcauto is called with the dataset (X, Y), setting 'CrossVal' to 'on' to enable cross-validation and using the CVPartition parameter to pass the cross-validation partition c created earlier.
- The model trained by fitcauto is a cross-validated model, encapsulating the models trained on each fold.
- model.Trained{1} is used to display the model from the first fold, as fitcauto returns a set of models, one for each fold.
- kfoldLoss is used to compute the cross-validated classification error, providing a measure of the model's performance.
This approach allows you to leverage the automatic model selection features of fitcauto while also benefiting from the robustness of k-fold cross-validation to evaluate the selected model's performance.
