empirical detection-error loss in matlab

To calculate empirical detection-error loss in MATLAB, you can use the following steps:

1. Create a binary classification model using your training data.

2. Predict the class labels of your validation data using this model.

3. Calculate the confusion matrix to determine the number of true positives, false positives, true negatives, and false negatives.

4. 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)

5. Calculate the average detection-error loss across all classes using the formula:

   • Avg_detection_error_loss = (detection_error_loss_1 + detection_error_loss_2) / 2

Here's an example code snippet to implement the above steps:

main.m
% Load and preprocess your data
[X_train,Y_train] = loadData('train.csv');
[X_val,Y_val] = loadData('val.csv');

% Train your binary classification model
model = fitcsvm(X_train,Y_train);

% Predict the class labels of your validation data
Y_pred = predict(model,X_val);

% Calculate the confusion matrix
C = confusionmat(Y_val,Y_pred);

% Calculate the detection-error loss for each class
false_negative_count_1 = C(1,2);
true_positive_count_1 = C(1,1);
detection_error_loss_1 = false_negative_count_1 / (false_negative_count_1 + true_positive_count_1);

false_positive_count_2 = C(2,1);
true_negative_count_2 = C(2,2);
detection_error_loss_2 = false_positive_count_2 / (false_positive_count_2 + true_negative_count_2);

% Calculate the average detection-error loss
Avg_detection_error_loss = (detection_error_loss_1 + detection_error_loss_2) / 2;
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