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<scheme description="How good are supervised data mining methods on your classification data set? Here's a workflow that scores various classification techniques on a data set from medicine. The central widget here is the one for testing and scoring, which is given the data and a set of learners, does cross-validation and scores predictive accuracy, and outputs the scores for further examination." title="Cross-Validation" version="2.0">
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		<link enabled="true" id="3" sink_channel="Learner" sink_node_id="1" source_channel="Learner" source_node_id="4" />
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		<text font-family="Helvetica" font-size="14" id="0" rect="(16.0, 56.0, 180.0, 82.0)">Choose class-labeled data set. Say, "iris.tab" from documentation data sets.</text>
		<text font-family="Helvetica" font-size="14" id="1" rect="(381.0, 76.0, 171.0, 54.0)">It's always a good idea to check out the data first.</text>
		<text font-family="Helvetica" font-size="14" id="2" rect="(586.0, 90.0, 197.0, 96.0)">Select a cell in confusion matrix to obtain related data instances. Here we examine them in the spreadheet.</text>
		<text font-family="Helvetica" font-size="14" id="3" rect="(533.0, 308.0, 150.0, 54.0)">Use for additional analysis of cross-validation results.</text>
		<text font-family="Helvetica" font-size="14" id="4" rect="(373.0, 319.0, 132.0, 82.0)">Cross-validation takes place here. Double click to see the performance scores.</text>
		<text font-family="Helvetica" font-size="14" id="5" rect="(42.0, 394.0, 150.0, 68.0)">Several learners can be scored in cross-validation at the same time.</text>
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