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<scheme description="Which classification technique works best on my data?
And how to inspect a model?&#10;&#10;Pass the bag of words matrix to Logistic
 Regression and inspect a model with Nomogram. To evaluate model's
 performance, use Test and Score. Inspect misclassifications from Confusion
 Matrix with Corpus Viewer."
 title="Model Evaluation on Text" version="2.0">
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		<link enabled="true" id="1" sink_channel="Corpus" sink_node_id="2" source_channel="Corpus" source_node_id="1" />
		<link enabled="true" id="2" sink_channel="Data" sink_node_id="3" source_channel="Corpus" source_node_id="2" />
		<link enabled="true" id="3" sink_channel="Classifier" sink_node_id="4" source_channel="Classifier" source_node_id="3" />
		<link enabled="true" id="4" sink_channel="Data" sink_node_id="5" source_channel="Corpus" source_node_id="2" />
		<link enabled="true" id="5" sink_channel="Learner" sink_node_id="5" source_channel="Learner" source_node_id="3" />
		<link enabled="true" id="6" sink_channel="Evaluation Results" sink_node_id="6" source_channel="Evaluation Results" source_node_id="5" />
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		<text font-family="Helvetica" font-size="16" id="1" rect="(69.0, 292.0, 168.0, 111.0)" type="text/plain">Provide the model to train the data on. You can replace Logistic Regression with any other learner for the Model pane.</text>
		<text font-family="Helvetica" font-size="16" id="2" rect="(405.0, 378.0, 223.0, 90.0)" type="text/plain">Nomogram visualizes model's classifications. It works only with Logistic Regression and Naive Bayes.</text>
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		<text font-family="Helvetica" font-size="16" id="5" rect="(600.0, 27.0, 180.0, 76.0)" type="text/plain">Open the widget to cross-validatated performance scores.</text>
		<text font-family="Helvetica" font-size="16" id="6" rect="(647.0, 303.0, 170.0, 97.0)" type="text/plain">Select misclassified documents in Confusion Matrix and inspect them in Corpus Viewer.</text>
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		<text font-family="Helvetica" font-size="16" id="9" rect="(263.0, 17.0, 150.0, 76.0)" type="text/plain">For speed we only keep 250 tokens. Does adding more help?</text>
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