error loss in matlab

To calculate the error or loss in machine learning or regression problems, you can use the loss function in MATLAB. The loss function returns the misclassification error or mean squared error between predicted and actual values. Here's an example:

main.m
% Generating sample data
X = randn(100, 10);
y = randn(100, 1);

% Creating a linear regression model
model = fitlm(X, y);

% Predicting new values using the model
y_pred = predict(model, X);

% Calculating the mean squared error between predicted and actual values
mse = loss(model, X, y);

% Displaying the mean squared error
disp(['Mean Squared Error: ', num2str(mse)])
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In the above code, we first generate sample data and create a linear regression model using fitlm function. Then, we predict new values using the model and calculate the mean squared error between predicted and actual values using loss function. Finally, we display the mean squared error using disp function.