how to calculate f-score,prediction and recall in matlab in matlab

To calculate f-score, precision and recall, you can use the confusionmat function in MATLAB, which creates a confusion matrix for classification problems. For example:

main.m
% True labels
y_true = [0 0 1 1 1 0 1 1 1 1];
% Predicted labels
y_pred = [0 1 1 1 0 0 1 1 1 1];

% Create a confusion matrix
conf_mat = confusionmat(y_true, y_pred);

% Calculate precision
precision = conf_mat(2,2) / sum(conf_mat(:,2));

% Calculate recall
recall = conf_mat(2,2) / sum(conf_mat(2,:));

% Calculate f-score (with beta = 1)
beta = 1;
f_score = (1 + beta^2) * (precision * recall) / ((beta^2 * precision) + recall);
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In this example, y_true represents the true class labels and y_pred represents the predicted class labels. The confusionmat function calculates a confusion matrix based on these labels, and conf_mat(2,2) refers to the number of true positives (i.e., the number of cases where the true label is positive and the predicted label is also positive).

Precision is calculated as the number of true positives divided by the total number of predicted positives. Recall is calculated as the number of true positives divided by the total number of actual positives. The f-score is a combination of precision and recall, with the beta parameter controlling the weighting of precision versus recall. In this case, we are using beta = 1 to give equal weight to precision and recall.