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<scheme description="How good are supervised data mining methods on your classification dataset? Here's a workflow that scores various classification techniques on a dataset 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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		<node id="2" name="Logistic Regression" position="(202.0, 245.0)" project_name="Orange3" qualified_name="Orange.widgets.model.owlogisticregression.OWLogisticRegression" title="Logistic Regression" version="" />
		<node id="3" name="Random Forest" position="(225.0, 328.0)" project_name="Orange3" qualified_name="Orange.widgets.model.owrandomforest.OWRandomForest" title="Random Forest Classification" version="" />
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		<link enabled="true" id="0" sink_channel="Data" sink_node_id="1" source_channel="Data" source_node_id="0" />
		<link enabled="true" id="1" sink_channel="Learner" sink_node_id="1" source_channel="Learner" source_node_id="2" />
		<link enabled="true" id="2" sink_channel="Learner" sink_node_id="1" source_channel="Learner" source_node_id="3" />
		<link enabled="true" id="3" sink_channel="Learner" sink_node_id="1" source_channel="Learner" source_node_id="4" />
		<link enabled="true" id="4" sink_channel="Data" sink_node_id="5" source_channel="Data" source_node_id="0" />
		<link enabled="true" id="5" sink_channel="Evaluation Results" sink_node_id="7" source_channel="Evaluation Results" source_node_id="1" />
		<link enabled="true" id="6" sink_channel="Data" sink_node_id="6" source_channel="Selected Data" source_node_id="7" />
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	<annotations>
		<text font-family="Helvetica" font-size="14" id="0" rect="(16.0, 56.0, 180.0, 82.0)" type="text/plain">Choose class-labeled dataset. Say, "iris.tab" from documentation datasets.</text>
		<text font-family="Helvetica" font-size="14" id="1" rect="(381.0, 76.0, 171.0, 54.0)" type="text/plain">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)" type="text/plain">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)" type="text/plain">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)" type="text/plain">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)" type="text/plain">Several learners can be scored in cross-validation at the same time.</text>
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