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selectThreshold.m
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selectThreshold.m
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function [bestEpsilon bestF1] = selectThreshold(yval, pval)
%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting
%outliers
% [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best
% threshold to use for selecting outliers based on the results from a
% validation set (pval) and the ground truth (yval).
%
bestEpsilon = 0;
bestF1 = 0;
F1 = 0;
stepsize = (max(pval) - min(pval)) / 1000;
for epsilon = min(pval):stepsize:max(pval)
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the F1 score of choosing epsilon as the
% threshold and place the value in F1. The code at the
% end of the loop will compare the F1 score for this
% choice of epsilon and set it to be the best epsilon if
% it is better than the current choice of epsilon.
%
% Note: You can use predictions = (pval < epsilon) to get a binary vector
% of 0's and 1's of the outlier predictions
predictions = (pval < epsilon);
%TP = size( find(predictions & yval) ,1);
%FP = size(predictions > yval ,1);
TP = sum((predictions == 1) & (yval == 1));
FP = sum((predictions == 1) & (yval == 0));
FN = sum((predictions == 0) & (yval == 1));
%FN = size(yval > predictions ,1);
prec = TP / ( TP + FP );
rec = TP / ( TP + FN) ;
F1 = 2 * prec * rec / ( prec + rec );
% =============================================================
if F1 > bestF1
bestF1 = F1;
bestEpsilon = epsilon;
end
end
end