ranking classifier in matlab

To rank classifiers in Matlab, you can use the built-in function perfcurve. This function computes the receiver operating characteristic (ROC) curve for binary classification problems and allows you to specify different ranking methods for the classifiers.

Here's an example code snippet that demonstrates how to use perfcurve to rank three different classifiers based on their area under the ROC curve (AUC):

main.m
% Generate some example data
rng(1); % For reproducibility 
X = randn(1000, 2);
y = X(:, 1) + X(:, 2) > 0;

% Fit three different classifiers
mdl1 = fitclinear(X, y);
mdl2 = fitctree(X, y);
mdl3 = fitcsvm(X, y);

% Compute ROC curves and AUCs for each classifier
[~, scores1] = predict(mdl1, X);
[~, scores2] = predict(mdl2, X);
[~, scores3] = predict(mdl3, X);
[X1, Y1, ~, AUC1] = perfcurve(y, scores1(:, 2), true);
[X2, Y2, ~, AUC2] = perfcurve(y, scores2(:, 2), true);
[X3, Y3, ~, AUC3] = perfcurve(y, scores3(:, 2), true);

% Plot ROC curves
plot(X1, Y1);
hold on;
plot(X2, Y2);
plot(X3, Y3);

% Print AUC values
fprintf('AUC (model 1) = %f\n', AUC1);
fprintf('AUC (model 2) = %f\n', AUC2);
fprintf('AUC (model 3) = %f\n', AUC3);
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In this example, we generate some example data, fit three different classifiers, and then use perfcurve to compute their ROC curves and AUCs. The predict function is used to obtain the scores (i.e., predicted probabilities of class 1) for each classifier on the training data. The perfcurve function is then used to compute the ROC curves and AUCs, with the true argument indicating that class 1 is considered the "positive" class. Finally, the ROC curves and AUC values are plotted and printed to the console.

Note that perfcurve supports several different ranking methods for binary classifiers, including probability estimates, predicted scores, decision values, and others. See the documentation for more details.