Hi Oujaoura,
The error you're encountering typically occurs when there's a mismatch in the way the predict function is being used. The predict function is expecting the model object as its first argument, but it seems like there might be a mistake in your code that's causing MATLAB to interpret something incorrectly.
Here’s a checklist and a corrected example to help you troubleshoot and resolve the issue:
Checklist:
- Model Variable: Ensure that nb is indeed your ClassificationNaiveBayes model object. Double-check that it hasn't been overwritten or modified in your code.
- Input Data: Make sure that values is a matrix with the same number of columns (features) as the data used to train nb. The number of features in values should match x.
- Function Syntax: Verify that the predict function is called correctly with the model object as the first argument.
Corrected Example:
Here's how you should be using the fitcnb and predict functions:
% Train the Naive Bayes model
x = rand(100, 5); % Example feature matrix with 100 samples and 5 features
t = randi([1, 3], 100, 1); % Example target vector with 3 classes
nb = fitcnb(x, t, 'Distribution', 'kernel');
% New data for prediction
values = rand(10, 5); % New data matrix with 10 samples and 5 features
% Predict class labels for new data
predictedLabels = predict(nb, values);
% Display predicted labels
disp('Predicted Class Labels:');
disp(predictedLabels);
Explanation:
- Training Data (x and t): Ensure x is your training feature matrix and t is your target vector.
- New Data (values): Ensure values has the same number of features as x.
- predict Function: Make sure the predict function is called with nb as the first argument and values as the second.
