Code covered by the BSD License
acc_cmp1(targs,preds1,pre... Tests the null hypothesis that two classifiers that were tested on
acc_cmp2(C1,C2) Tests the null hypothesis that two classifiers that were tested on
acc_mean(C) Expected value of the accuracy (i.e., the mean of a Beta distribution).
acc_med(C) Median of the accuracy (i.e., the median of a Beta distribution).
acc_mode(C) Most likely accuracy (i.e., the mode of a Beta distribution)
acc_p(C) P value of the Null hypothesis that the accuracy is not significantly
acc_ppi(C,alpha) Posterior probability interval of the accuracy.
acc_sem(C) Naive standard-error-of-the-mean of the across-test-cases mean accuracy.
adjustErrorBarWidth(hE, w... Adjusts error bar widths. Use in conjunction with errorbar().
bacc_demo Simple demo to compare accuracies and balanced accuracies.
bacc_mean(C) Expected value of the balanced accuracy (i.e., the mean of the average of
bacc_med(C) Median of the balanced accuracy (i.e., the median of the average of
bacc_mode(C) Most likely balanced accuracy (i.e., the mode of the average of
bacc_naive(C) Naive balanced accuracy (simply the mean of the individual accuracies,
bacc_p(C) P value of the Null hypothesis that the balanced accuracy is not
bacc_ppi(C,alpha) Posterior probability interval of the balanced accuracy.
betaavgcdf(x, alpha1, bet... CDF of the average of two independent random variables which are
betaavginv(y, alpha1, bet... Inverse CDF of the sum of two independent random variables which are
betaavgpdf(x, alpha1, bet... PDF of the average of two independent random variables which are
betaconv(res, alpha1, bet... Convolves two Beta distributions.
betasumcdf(x, alpha1, bet... CDF of the sum of two independent random variables which are distributed
betasumpdf(x, alpha1, bet... PDF of the sum of two independently distributed Beta distributions.
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Computing the posterior balanced accuracy
by
Kay H. Brodersen
02 Nov 2010
A set of MATLAB functions for evaluating generalization performance in binary classification.
acc_med(C)
% Median of the accuracy (i.e., the median of a Beta distribution).
%
% Usage:
% a_med = acc_med(C)
%
% Arguments:
% C - 2x2 confusion matrix of classification outcomes
% Kay H. Brodersen, ETH Zurich, Switzerland
% http://people.inf.ethz.ch/bkay/
% $Id: acc_med.m 8245 2010-10-22 12:57:51Z bkay $
% -------------------------------------------------------------------------
function a_med = acc_med(C)
% Get alpha and beta
A = C(1,1) + C(2,2) + 1;
B = C(1,2) + C(2,1) + 1;
% Compute median
a_med = betainv(0.5, A, B);
end
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