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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">ROC</b><td valign="baseline" align="right" class="function"><a href="../misc/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>computes Receiver Operating Characteristic (ROC) curves. </b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Synopsis:</span></span><br><span class=help> [FP,FN]=roc(dfce,y)</span><br><span class=help> </span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> It computes false positive rate FP and false negative rate FN</span><br><span class=help> with rescpect to the shift of the bias of given binary decision </span><br><span class=help> function. The values of the decision function are given in dfce </span><br><span class=help> and y contains true labels (number 1 and/or 2). The vectors dfce </span><br><span class=help> and y must be of the same length. </span><br><span class=help> The bias is shifted from min(dfce) to max(dfce). </span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> dfce [1 x num_data] Values of decision function returned by </span><br><span class=help> a classifier.</span><br><span class=help> y [1 x num_data] True labels.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> FP [1 x num_data] False positive rate.</span><br><span class=help> FN [1 x num_data] False negative rate.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = load('riply_trn');</span><br><span class=help> model = fld(data);</span><br><span class=help> [y_pred,dfce] = linclass(data.X,model);</span><br><span class=help> [FP,FN] = roc(dfce,data.y);</span><br><span class=help> figure; hold on; plot(FP,FN);</span><br><span class=help> xlabel('false positives'); </span><br><span class=help> ylabel('false negatives');</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also> <a href = "../misc/cerror.html" target="mdsbody">CERROR</a></span><br><span class=help><span class=also></span><br></code></div> <hr> <b>Source:</b> <a href= "../misc/list/roc.html">roc.m</a> <p><b class="info_field">(c) </b> Statistical Pattern Recognition Toolbox, (C) 1999-2003,<br> Written by Vojtech Franc and Vaclav Hlavac,<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a>,<br> <a href="http://www.feld.cvut.cz">Faculty of Electrical engineering</a>,<br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 26-aug-2005, VF<br> 17-may-2004, VF<br> 6-June-2003, VF<br> 24-Feb-2003, VF<br></body></html>?? 快捷鍵說明
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