To calculate empirical detection-error loss in MATLAB, you can use the following steps:
Create a binary classification model using your training data.
Predict the class labels of your validation data using this model.
Calculate the confusion matrix to determine the number of true positives, false positives, true negatives, and false negatives.
Calculate the detection-error loss for each class as follows:
For class 1: detection_error_loss_1 = false_negative_count_1 / (false_negative_count_1 + true_positive_count_1)
For class 2: detection_error_loss_2 = false_positive_count_2 / (false_positive_count_2 + true_negative_count_2)
Calculate the average detection-error loss across all classes using the formula:
Here's an example code snippet to implement the above steps:
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