function loss in matlab

To define a custom loss function in MATLAB, you can use the anonymous function syntax to create a function handle. The loss function should take in the model predictions and the actual target values, and output the scalar value of the loss. Here's an example of a mean squared error (MSE) loss function:

main.m
loss = @(Ypred, Ytrue) mean((Ypred - Ytrue).^2);
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Once you have defined the loss function, you can use it with optimization functions such as fminunc to minimize the loss and find the optimal model parameters:

main.m
theta0 = randn(1, p); % initialize parameters
theta_opt = fminunc(@(theta) loss(predict(X, theta), Y), theta0);
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In the above example, predict is a function that takes in the input matrix X and the model parameters theta, and outputs the model predictions Ypred. The loss function is then evaluated on Ypred and the actual target values Y, and the optimization function tries to find the optimal theta that minimizes the loss.