create an evaluation function for macro f1 score in matlab

To create an evaluation function for the macro F1 score in MATLAB, you can follow these steps:

1. Define a function that takes two inputs: the predicted labels and the actual labels.
2. Convert the labels to binary matrices using one-hot encoding or binary conversion.
3. Calculate the precision, recall, and F1 score for each class.
4. Average the F1 scores across all classes to obtain the macro F1 score.
5. Return the macro F1 score as the output of the function.

Here's an example implementation:

main.m
function macro_f1 = calculateMacroF1(predicted_labels, actual_labels)
    num_classes = size(predicted_labels, 2);
    f1_scores = zeros(1, num_classes);
    
    for class_idx = 1:num_classes
        predicted_class_labels = predicted_labels(:, class_idx);
        actual_class_labels = actual_labels(:, class_idx);
        
        tp = sum(predicted_class_labels & actual_class_labels);
        fp = sum(predicted_class_labels & ~actual_class_labels);
        fn = sum(~predicted_class_labels & actual_class_labels);
        
        precision = tp / (tp + fp);
        recall = tp / (tp + fn);
        f1_scores(class_idx) = 2 * (precision * recall) / (precision + recall);
    end
    
    macro_f1 = mean(f1_scores);
end
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You can use this calculateMacroF1 function by passing in the predicted labels and actual labels as inputs. Make sure the labels are in the form of a binary matrix, where each column represents a class and each row represents an example. The function will output the macro F1 score.

Note: This implementation assumes binary classification for each class. If you have multi-class classification, you may need to modify the function accordingly.

Remember to preprocess your data and transform it into the required format before using this evaluation function.