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<scheme description="How good are supervised data mining methods on your classification data set? Here's a schema that scores various classification techniques on a data set from medicine. A central widget here is the one for evaluation, 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="k Nearest Neighbours" position="(170.0, 448.0)" project_name="Orange" qualified_name="Orange.OrangeWidgets.Classify.OWKNN.OWKNN" title="k Nearest Neighbours" version="" />
		<node id="3" name="Random Forest" position="(169.0, 356.0)" project_name="Orange" qualified_name="Orange.OrangeWidgets.Classify.OWRandomForest.OWRandomForest" title="Random Forest" version="" />
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		<node id="6" name="Confusion Matrix" position="(535.0, 188.0)" project_name="Orange" qualified_name="Orange.OrangeWidgets.Evaluate.OWConfusionMatrix.OWConfusionMatrix" title="Confusion Matrix" version="" />
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		<link enabled="true" id="0" sink_channel="Data" sink_node_id="5" source_channel="Data" source_node_id="0" />
		<link enabled="true" id="1" sink_channel="Learner" sink_node_id="4" source_channel="Learner" source_node_id="1" />
		<link enabled="true" id="2" sink_channel="Learner" sink_node_id="4" source_channel="Learner" source_node_id="3" />
		<link enabled="true" id="3" sink_channel="Learner" sink_node_id="4" source_channel="Learner" source_node_id="2" />
		<link enabled="true" id="4" sink_channel="Data" sink_node_id="4" source_channel="Data" source_node_id="0" />
		<link enabled="true" id="5" sink_channel="Evaluation Results" sink_node_id="6" source_channel="Evaluation Results" source_node_id="4" />
		<link enabled="true" id="6" sink_channel="Data" sink_node_id="7" source_channel="Selected Data" source_node_id="6" />
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		<text font-family="Helvetica" font-size="12" id="0" rect="(34.0, 30.0, 96.0, 48.0)">Class-labeled
data set</text>
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		<arrow end="(262.0, 411.0)" fill="#C1272D" id="2" start="(309.0, 458.0)" />
		<text font-family="Helvetica" font-size="12" id="3" rect="(319.0, 446.0, 150.0, 48.0)">Several learners can be
scored in cross-validation
at the same time.</text>
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		<text font-family="Helvetica" font-size="12" id="5" rect="(413.0, 44.0, 150.0, 36.0)">It's always a good idea to check out the data first.</text>
		<arrow end="(360.0, 239.0)" fill="#C1272D" id="6" start="(378.0, 294.0)" />
		<text font-family="Helvetica" font-size="12" id="7" rect="(305.0, 302.0, 150.0, 60.0)">Cross-validation takes place here. Double click to see the performance scores.</text>
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		<text font-family="Helvetica" font-size="12" id="9" rect="(510.0, 309.0, 159.0, 48.0)">Use for additional analysis of cross-validation results.</text>
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		<text font-family="Helvetica" font-size="12" id="11" rect="(624.0, 51.0, 163.0, 64.0)">Select a cell in confusion matrix to obtain related data instances. Here we examine them in the spreadheet.</text>
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