# design the function function loss = loss_func(t_hat, t) that computes the empirical loss f binary predictions t_hat in matlab

```main.m```function loss = loss_func(t_hat, t)
% Calculates the empirical loss of binary predictions t_hat
% t_hat: predicted labels
% t: true labels
% loss: empirical loss

% Calculate the loss using the cross-entropy loss function
loss = -t.*log(t_hat) - (1-t).*log(1-t_hat);

% Take the mean of the losses
loss = mean(loss);

end
```322 chars14 lines```

Explanation:

• The input t_hat represents the predicted binary labels, and t represents the true binary labels.
• The function uses the cross-entropy loss function to calculate the loss.
• The cross-entropy loss function is defined as:
```main.m```L = -t*log(t_hat) - (1-t)*log(1-t_hat)
```39 chars2 lines```
• The loss is calculated for each point and then taking the mean of these losses to calculate the empirical loss of the whole set of predictions.
• The function returns the value of empirical loss, which quantifies how far the predicted labels are from the true labels.

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