Hi Reuben,
Here's a basic guide on how you might handle training, validation, and testing of a model in MATLAB, using a simple example with a classification model. MATLAB provides several built-in functions for machine learning, including the fitcsvm function for training a support vector machine (SVM) classifier.
Steps in MATLAB
- Load and Split Data: First, split your dataset into training, validation, and test sets.
- Train the Model: Use the training set to train your model.
- Validate the Model: Use the validation set to tune hyperparameters and check for overfitting.
- Test the Model: Use the test set to evaluate the final model's performance.
Example Code
Here's a simple example using MATLAB:
% Load your dataset
load fisheriris % Example dataset
X = meas; % Features
y = species; % Labels
% Split the data into training, validation, and test sets
cv = cvpartition(length(y), 'HoldOut', 0.4);
XTrain = X(training(cv), :);
yTrain = y(training(cv), :);
XTemp = X(test(cv), :);
yTemp = y(test(cv), :);
% Further split the temp data into validation and test
cv2 = cvpartition(length(yTemp), 'HoldOut', 0.5);
XVal = XTemp(training(cv2), :);
yVal = yTemp(training(cv2), :);
XTest = XTemp(test(cv2), :);
yTest = yTemp(test(cv2), :);
% Train the model using the training set
model = fitcsvm(XTrain, yTrain);
% Validate the model using the validation set
valPredictions = predict(model, XVal);
valAccuracy = sum(valPredictions == yVal) / length(yVal);
fprintf('Validation Accuracy: %.2f%%\n', valAccuracy * 100);
% Test the model using the test set
testPredictions = predict(model, XTest);
testAccuracy = sum(testPredictions == yTest) / length(yTest);
fprintf('Test Accuracy: %.2f%%\n', testAccuracy * 100);
% Display a detailed classification report
confMat = confusionmat(yTest, testPredictions);
disp('Confusion Matrix:');
disp(confMat);
Explanation
- Data Splitting: We first split the data into training and temporary sets (60% training, 40% temporary). The temporary set is further split into validation and test sets (20% each).
- Model Training: We train an SVM model using the training data.
- Validation: We evaluate the model's performance on the validation set to ensure it generalizes well and to fine-tune parameters if necessary.
- Testing: Finally, we assess the model's performance on the test set to get an unbiased estimate of its accuracy.
