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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual confmat
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> confmat
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Compute a confusion matrix.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 [C, rate] = confmat(y, t)</PRE>
wolffd@0 20
wolffd@0 21
wolffd@0 22 <p><h2>
wolffd@0 23 Description
wolffd@0 24 </h2>
wolffd@0 25 <CODE>[C, rate] = confmat(y, t)</CODE> computes the confusion matrix <CODE>C</CODE>
wolffd@0 26 and classification performance <CODE>rate</CODE> for the predictions mat{y}
wolffd@0 27 compared with the targets <CODE>t</CODE>. The data is assumed to be in a
wolffd@0 28 1-of-N encoding, unless there is just one column, when it is assumed to
wolffd@0 29 be a 2 class problem with a 0-1 encoding. Each row of <CODE>y</CODE> and <CODE>t</CODE>
wolffd@0 30 corresponds to a single example.
wolffd@0 31
wolffd@0 32 <p>In the confusion matrix, the rows represent the true classes and the
wolffd@0 33 columns the predicted classes. The vector <CODE>rate</CODE> has two entries:
wolffd@0 34 the percentage of correct classifications and the total number of
wolffd@0 35 correct classifications.
wolffd@0 36
wolffd@0 37 <p><h2>
wolffd@0 38 See Also
wolffd@0 39 </h2>
wolffd@0 40 <CODE><a href="conffig.htm">conffig</a></CODE>, <CODE><a href="demtrain.htm">demtrain</a></CODE><hr>
wolffd@0 41 <b>Pages:</b>
wolffd@0 42 <a href="index.htm">Index</a>
wolffd@0 43 <hr>
wolffd@0 44 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 45
wolffd@0 46
wolffd@0 47 </body>
wolffd@0 48 </html>