wolffd@0: wolffd@0:
wolffd@0:wolffd@0: [C, rate] = confmat(y, t)wolffd@0: wolffd@0: wolffd@0:
[C, rate] = confmat(y, t)
computes the confusion matrix C
wolffd@0: and classification performance rate
for the predictions mat{y}
wolffd@0: compared with the targets t
. The data is assumed to be in a
wolffd@0: 1-of-N encoding, unless there is just one column, when it is assumed to
wolffd@0: be a 2 class problem with a 0-1 encoding. Each row of y
and t
wolffd@0: corresponds to a single example.
wolffd@0:
wolffd@0: In the confusion matrix, the rows represent the true classes and the
wolffd@0: columns the predicted classes. The vector rate
has two entries:
wolffd@0: the percentage of correct classifications and the total number of
wolffd@0: correct classifications.
wolffd@0:
wolffd@0:
conffig
, demtrain
Copyright (c) Ian T Nabney (1996-9) wolffd@0: wolffd@0: wolffd@0: wolffd@0: