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<scheme description="PCA transforms the data into a dataset with uncorrelated variables, also called principal components. PCA widget displays a graph (scree diagram) showing a degree of explained variance by best principal components and allows to interactively set the number of components to be included in the output dataset. In this workflow, we can observe the transformation in the Data Table and visualize the data using the constructed principal components in the Scatter Plot." title="Principal Component Analysis" version="2.0">
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		<text font-family="Helvetica" font-size="12" id="0" rect="(152.0, 63.0, 150.0, 60.0)">Open to see the scree diagram and interactively select the number of components.</text>
		<text font-family="Helvetica" font-size="12" id="1" rect="(346.0, 9.0, 150.0, 72.0)">Choose two best principal components and check if the classes from the input dataset are well separated.</text>
		<text font-family="Helvetica" font-size="12" id="2" rect="(37.0, 302.0, 209.0, 58.0)">The File widget loads brown-selected, a dataset from molecular biology with 79 features, 186 instances and 3 classes.</text>
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